#load("vcomball20210902.Rda")
load(path(here::here("InitalDataCleaning/Data/vcomball20210902.Rda")))
d <- vcomball
# load("vsurvall20210902.Rda")
# d <- vsurvall

#load("vsiteid20210601.Rda")
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
new.d.1 <- data.frame(matrix(ncol=0, nrow=nrow(d)))

SITE ID

  • Codes(based on Surveyid)
    • 10 Greater CA
    • 20 Georgia
    • 25 North Carolina
    • 30 Northern CA
    • 40 Louisiana
    • 50 New Jersey
    • 60 Detroit
    • 61 Michigan
    • 70 Texas
    • 80 Los Angeles County
    • 81 USC-Other
    • 82 USC-MEC
    • 90 New York
    • 94 Florida
    • 95 WebRecruit-Limbo
    • 99 WebRecruit
  siteid <- as.factor(trimws(d[,"siteid"]))
  #new.d.n <- data.frame(new.d.n, siteid) # keep NAACCR coding
  
  levels(siteid)[levels(siteid)=="80"] <- "Los Angeles County.80"
  levels(siteid)[levels(siteid)=="30"] <- "Northern CA.30"
  levels(siteid)[levels(siteid)=="10"] <- "Greater CA.10"
  levels(siteid)[levels(siteid)=="60"] <- "Detroit.60"
  levels(siteid)[levels(siteid)=="40"] <- "Louisiana.40"
  levels(siteid)[levels(siteid)=="20"] <- "Georgia.20"
  levels(siteid)[levels(siteid)=="61"] <- "Michigan.61"
  levels(siteid)[levels(siteid)=="50"] <- "New Jersey.50"
  levels(siteid)[levels(siteid)=="70"] <- "Texas.70"
  levels(siteid)[levels(siteid)=="99"] <- "WebRecruit.99"
  levels(siteid)[levels(siteid)=="21"] <- "Georgia.21"
  levels(siteid)[levels(siteid)=="81"] <- "USC Other.81"
  levels(siteid)[levels(siteid)=="82"] <- "USC MEC.82"

  siteid_new<- siteid
  d<-data.frame(d, siteid_new)
  new.d <- data.frame(new.d, siteid)
  new.d <- apply_labels(new.d, siteid = "Site ID")
  new.d.1 <- data.frame(new.d.1, siteid)
  siteid_count<-count(new.d$siteid)
  colnames(siteid_count)<- c("Registry", "Total")
  kable(siteid_count, format = "simple", align = 'l', caption = "Overview of all Registries")
d<-d[which(d$siteid_new == params$site),]
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
#new.d<-new.d[which(new.d$siteid == params$site),]

SURVEY ID

  • Scantron assigned SurveyID
  surveyid <- as.factor(d[,"surveyid"])
  isDup <- duplicated(surveyid)
  numDups <- sum(isDup)
  dups <- surveyid[isDup]
  
  new.d <- data.frame(new.d, surveyid)
  new.d <- apply_labels(new.d, surveyid = "Survey ID")
  
  print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 0"
  print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
  print(dups)
## factor(0)
## 282 Levels: 100050  100059  100061  100064  100072  100073  100080  100084  100088  100092  100096  100098  ... 102011
  print("Number of NAs:")
## [1] "Number of NAs:"
  print(sum(is.na(new.d$surveyid)))
## [1] 0

LOCATION NAME

  • Name of Registry delivery location
  locationname <- as.factor(d[,"locationname"])
  
  new.d <- data.frame(new.d, locationname)
  new.d <- apply_labels(new.d, locationname = "Recruitment Location")
  temp.d <- data.frame (new.d, locationname)

  result<-questionr::freq(temp.d$locationname, total = TRUE)
  #Create a NICE table
  kable(result, format = "simple", align = 'l', caption = "Overview of Registry delivery location")
Overview of Registry delivery location
n % val%
Greater California 282 100 100
Total 282 100 100

RESPOND ID

  • From Barcode label put on last page of survey by registries, identifies participant. ResponseID is assigned by the registries.
  respondid <- as.factor(d[,"respondid"])
  #remove NAs in respondid in order to avoid showing NAs in duplicated values
  respondid_rm<-respondid[!is.na(respondid)]
  isDup <- duplicated(respondid_rm)
  numDups <- sum(isDup)
  dups <- respondid_rm[isDup]
  
  new.d <- data.frame(new.d, respondid)
  new.d <- apply_labels(new.d, respondid = "RESPOND ID")
  
  print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 1"
  print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
  print(dups)
## [1] 10101033
## 281 Levels: 10100003 10100012 10100023 10100024 10100027 10100042 10100047 10100048 10100052 10100054 ... 10101147
  print("Number of NAs:")
## [1] "Number of NAs:"
  print(sum(is.na(new.d$respondid)))
## [1] 0

METHODOLOGY

  • How survey was completed
    • P=Paper
    • O=Online complete
st_css()
  methodology <- as.factor(d[,"methodology"])
  levels(methodology) <- list(Paper="P",
                              Online="O")
  methodology <- ordered(methodology, c("Paper", "Online"))
  new.d <- data.frame(new.d, methodology)
  new.d <- apply_labels(new.d, methodology = "Methodology for Survey Completion")
  temp.d <- data.frame (new.d, methodology)  
  
  result<-questionr::freq(temp.d$methodology, total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val%
Paper 282 100 100
Online 0 0 0
Total 282 100 100

A1: Date of diagnosis

  • A1. In what month and year were you first diagnosed with prostate cancer?
# a1month
a1month <- as.factor(d[,"a1month"])
  
  new.d <- data.frame(new.d, a1month)
  new.d <- apply_labels(new.d, a1month = "Month Diagnosed")
  temp.d <- data.frame (new.d, a1month) 
  
  result<-questionr::freq(temp.d$a1month, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")
A1:month diagnosed
n % val%
1 14 5.0 5.6
10 26 9.2 10.4
11 16 5.7 6.4
12 16 5.7 6.4
2 18 6.4 7.2
3 17 6.0 6.8
4 26 9.2 10.4
5 22 7.8 8.8
6 43 15.2 17.3
7 20 7.1 8.0
8 14 5.0 5.6
9 17 6.0 6.8
NA 33 11.7 NA
Total 282 100.0 100.0 
  #count<-as.data.frame(table(new.d$a1month))
  #colnames(count)<- c("a1month", "Total")
  #freq1<-table(new.d$a1month)
  #freq<-as.data.frame(round(prop.table(freq1),3))
  #colnames(freq)<- c("a1month", "Freq")
  #result<-merge(count, freq,by="a1month",sort=F)
  #kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")

#a1year
  tmp<-d[,"a1year"]
  tmp[tmp=="15"]<-"2015"
  a1year <- as.factor(tmp)
  #levels(a1year)[levels(a1year)=="15"] <- "2015"
  #a1year[a1year=="15"] <- "2015"  # change "15" to "2015"
  #a1year <- as.Date(a1year, format = "%Y")
  #a1year <- relevel(a1year, ref="1914")

  new.d <- data.frame(new.d, a1year)
  new.d <- apply_labels(new.d, a1year = "Year Diagnosed")
  temp.d <- data.frame (new.d, a1year) 

  result<-questionr::freq(temp.d$a1year, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:year diagnosed")
A1:year diagnosed
n % val%
19 1 0.4 0.4
1915 1 0.4 0.4
1917 1 0.4 0.4
1918 1 0.4 0.4
1945 1 0.4 0.4
1950 1 0.4 0.4
1951 1 0.4 0.4
1994 1 0.4 0.4
2002 1 0.4 0.4
2004 1 0.4 0.4
2005 2 0.7 0.8
2009 1 0.4 0.4
2010 2 0.7 0.8
2012 2 0.7 0.8
2013 4 1.4 1.6
2014 13 4.6 5.0
2015 78 27.7 30.2
2016 112 39.7 43.4
2017 24 8.5 9.3
2018 6 2.1 2.3
2019 3 1.1 1.2
2020 1 0.4 0.4
NA 24 8.5 NA
Total 282 100.0 100.0 
  #a1not
# 1=I have NEVER had prostate cancer
# 2=I HAVE or HAVE HAD prostate cancer
# (paper survey only had a bubble for “never had” so value set to 2 if bubble not marked)"
  a1not <- as.factor(d[,"a1not"])
  levels(a1not) <- list(NEVER_had_ProstateCancer="1",
                         HAVE_had_ProstateCancer="2")
  new.d <- data.frame(new.d, a1not)
  new.d <- apply_labels(new.d, a1not = "Not Diagnosed")
  temp.d <- data.frame (new.d, a1not) 

  result<-questionr::freq(temp.d$a1not, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:not diagnosed")
A1:not diagnosed
n % val%
NEVER_had_ProstateCancer 0 0 0
HAVE_had_ProstateCancer 282 100 100
Total 282 100 100

A2: Identify as AA

  • A2. Do you identify as Black or African American?
    • 2=Yes
    • 1=No
a2 <- as.factor(d[,"a2"])
# Make "*" to NA
a2[which(a2=="*")]<-"NA"
levels(a2) <- list(No="1",
                   Yes="2")
  a2 <- ordered(a2, c("Yes","No"))
  
  new.d <- data.frame(new.d, a2)
  new.d <- apply_labels(new.d, a2 = "Month Diagnosed")
  temp.d <- data.frame (new.d, a2) 
  
  result<-questionr::freq(temp.d$a2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A2")
A2
n % val%
Yes 253 89.7 99.2
No 2 0.7 0.8
NA 27 9.6 NA
Total 282 100.0 100.0

A3: Black or African American group

  • A3. If Yes: A2. Which Black or African American group(s) and other races/ethnicities do you identify with? Mark all that apply.
    • A3_1: 1=Black/African American
    • A3_2: 1=Nigerian
    • A3_3: 1=Jamaican
    • A3_4: 1=Ethiopian
    • A3_5: 1=Haitian
    • A3_6: 1=Somali
    • a3_7: 1=Guyanese
    • A3_8: 1=Creole
    • A3_9: 1=West Indian
    • A3_10: 1=Caribbean
    • A3_11: 1=White
    • A3_12: 1=Asian/Asian American
    • A3_13: 1=Native American or American Indian or Alaskan Native
    • A3_14: 1=Middle Eastern or North African
    • A3_15: 1=Native Hawaiian or Pacific Islander
    • A3_16: 1=Hispanic
    • A3_17: 1=Latino
    • A3_18: 1=Spanish
    • A3_19: 1=Mexican/Mexican American
    • A3_20: 1=Salvadoran
    • A3_21: 1=Puerto Rican
    • A3_22: 1=Dominican
    • A3_23: 1=Columbian
    • A3_24: 1=Other
a3_1 <- as.factor(d[,"a3_1"])
  levels(a3_1) <- list(Black_African_American="1")
  new.d <- data.frame(new.d, a3_1)
  new.d <- apply_labels(new.d, a3_1 = "Black_African_American")
  temp.d <- data.frame (new.d, a3_1)
  result<-questionr::freq(temp.d$a3_1, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Black_African_American")
1. Black_African_American
n % val%
Black_African_American 247 87.6 100
NA 35 12.4 NA
Total 282 100.0 100 
a3_2 <- as.factor(d[,"a3_2"])
  levels(a3_2) <- list(Nigerian="1")
  new.d <- data.frame(new.d, a3_2)
  new.d <- apply_labels(new.d, a3_2 = "Nigerian")
  temp.d <- data.frame (new.d, a3_2)
  result<-questionr::freq(temp.d$a3_2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Nigerian")
2. Nigerian
n % val%
Nigerian 17 6 100
NA 265 94 NA
Total 282 100 100 
a3_3 <- as.factor(d[,"a3_3"])
  levels(a3_3) <- list(Jamaican="1")
  new.d <- data.frame(new.d, a3_3)
  new.d <- apply_labels(new.d, a3_3 = "Jamaican")
  temp.d <- data.frame (new.d, a3_3)
  result<-questionr::freq(temp.d$a3_3, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Jamaican")
3. Jamaican
n % val%
Jamaican 5 1.8 100
NA 277 98.2 NA
Total 282 100.0 100 
a3_4 <- as.factor(d[,"a3_4"])
  levels(a3_4) <- list(Ethiopian="1")
  new.d <- data.frame(new.d, a3_4)
  new.d <- apply_labels(new.d, a3_4 = "Ethiopian")
  temp.d <- data.frame (new.d, a3_4)
  result<-questionr::freq(temp.d$a3_4, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Ethiopian")
4. Ethiopian
n % val%
Ethiopian 0 0 NaN
NA 282 100 NA
Total 282 100 100 
a3_5 <- as.factor(d[,"a3_5"])
  levels(a3_5) <- list(Haitian="1")
  new.d <- data.frame(new.d, a3_5)
  new.d <- apply_labels(new.d, a3_5 = "Haitian")
  temp.d <- data.frame (new.d, a3_5)
  result<-questionr::freq(temp.d$a3_5, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Haitian")
5. Haitian
n % val%
Haitian 0 0 NaN
NA 282 100 NA
Total 282 100 100 
a3_6 <- as.factor(d[,"a3_6"])
  levels(a3_6) <- list(Somali="1")
  new.d <- data.frame(new.d, a3_6)
  new.d <- apply_labels(new.d, a3_6 = "Somali")
  temp.d <- data.frame (new.d, a3_6)
  result<-questionr::freq(temp.d$a3_6, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Somali")
6. Somali
n % val%
Somali 0 0 NaN
NA 282 100 NA
Total 282 100 100 
a3_7 <- as.factor(d[,"a3_7"])
  levels(a3_7) <- list(Guyanese="1")
  new.d <- data.frame(new.d, a3_7)
  new.d <- apply_labels(new.d, a3_7 = "Guyanese")
  temp.d <- data.frame (new.d, a3_7)
  result<-questionr::freq(temp.d$a3_7, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Guyanese")
7. Guyanese
n % val%
Guyanese 0 0 NaN
NA 282 100 NA
Total 282 100 100 
a3_8 <- as.factor(d[,"a3_8"])
  levels(a3_8) <- list(Creole="1")
  new.d <- data.frame(new.d, a3_8)
  new.d <- apply_labels(new.d, a3_8 = "Creole")
  temp.d <- data.frame (new.d, a3_8)
  result<-questionr::freq(temp.d$a3_8, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Creole")
8. Creole
n % val%
Creole 4 1.4 100
NA 278 98.6 NA
Total 282 100.0 100 
a3_9 <- as.factor(d[,"a3_9"])
  levels(a3_9) <- list(West_Indian="1")
  new.d <- data.frame(new.d, a3_9)
  new.d <- apply_labels(new.d, a3_9 = "West_Indian")
  temp.d <- data.frame (new.d, a3_9)
  result<-questionr::freq(temp.d$a3_9, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. West_Indian")
9. West_Indian
n % val%
West_Indian 3 1.1 100
NA 279 98.9 NA
Total 282 100.0 100 
a3_10 <- as.factor(d[,"a3_10"])
  levels(a3_10) <- list(Caribbean="1")
  new.d <- data.frame(new.d, a3_10)
  new.d <- apply_labels(new.d, a3_10 = "Caribbean")
  temp.d <- data.frame (new.d, a3_10)
  result<-questionr::freq(temp.d$a3_10, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Caribbean")
10. Caribbean
n % val%
Caribbean 4 1.4 100
NA 278 98.6 NA
Total 282 100.0 100 
a3_11 <- as.factor(d[,"a3_11"])
  levels(a3_11) <- list(White="1")
  new.d <- data.frame(new.d, a3_11)
  new.d <- apply_labels(new.d, a3_11 = "White")
  temp.d <- data.frame (new.d, a3_11)
  result<-questionr::freq(temp.d$a3_11, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "11. White")
11. White
n % val%
White 11 3.9 100
NA 271 96.1 NA
Total 282 100.0 100 
a3_12 <- as.factor(d[,"a3_12"])
  levels(a3_12) <- list(Asian="1")
  new.d <- data.frame(new.d, a3_12)
  new.d <- apply_labels(new.d, a3_12 = "Asian")
  temp.d <- data.frame (new.d, a3_12)
  result<-questionr::freq(temp.d$a3_12, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "12. Asian")
12. Asian
n % val%
Asian 1 0.4 100
NA 281 99.6 NA
Total 282 100.0 100 
a3_13 <- as.factor(d[,"a3_13"])
  levels(a3_13) <- list(Native_Indian="1")
  new.d <- data.frame(new.d, a3_13)
  new.d <- apply_labels(new.d, a3_13 = "Native_Indian")
  temp.d <- data.frame (new.d, a3_13)
  result<-questionr::freq(temp.d$a3_13, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "13. Native_Indian")
13. Native_Indian
n % val%
Native_Indian 12 4.3 100
NA 270 95.7 NA
Total 282 100.0 100 
a3_14 <- as.factor(d[,"a3_14"])
  levels(a3_14) <- list(Middle_Eastern_North_African="1")
  new.d <- data.frame(new.d, a3_14)
  new.d <- apply_labels(new.d, a3_14 = "Middle_Eastern_North_African")
  temp.d <- data.frame (new.d, a3_14)
  result<-questionr::freq(temp.d$a3_14, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "14. Middle_Eastern_North_African")
14. Middle_Eastern_North_African
n % val%
Middle_Eastern_North_African 0 0 NaN
NA 282 100 NA
Total 282 100 100 
a3_15 <- as.factor(d[,"a3_15"])
  levels(a3_15) <- list(Native_Hawaiian_Pacific_Islander="1")
  new.d <- data.frame(new.d, a3_15)
  new.d <- apply_labels(new.d, a3_15 = "Native_Hawaiian_Pacific_Islander")
  temp.d <- data.frame (new.d, a3_15)
  result<-questionr::freq(temp.d$a3_15, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "15. Native_Hawaiian_Pacific_Islander")
15. Native_Hawaiian_Pacific_Islander
n % val%
Native_Hawaiian_Pacific_Islander 0 0 NaN
NA 282 100 NA
Total 282 100 100 
a3_16 <- as.factor(d[,"a3_16"])
  levels(a3_16) <- list(Hispanic="1")
  new.d <- data.frame(new.d, a3_16)
  new.d <- apply_labels(new.d, a3_16 = "Hispanic")
  temp.d <- data.frame (new.d, a3_16)
  result<-questionr::freq(temp.d$a3_16, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "16. Hispanic")
16. Hispanic
n % val%
Hispanic 4 1.4 100
NA 278 98.6 NA
Total 282 100.0 100 
a3_17 <- as.factor(d[,"a3_17"])
  levels(a3_17) <- list(Latino="1")
  new.d <- data.frame(new.d, a3_17)
  new.d <- apply_labels(new.d, a3_17 = "Latino")
  temp.d <- data.frame (new.d, a3_17)
  result<-questionr::freq(temp.d$a3_17, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "17. Latino")
17. Latino
n % val%
Latino 1 0.4 100
NA 281 99.6 NA
Total 282 100.0 100 
a3_18 <- as.factor(d[,"a3_18"])
  levels(a3_18) <- list(Spanish="1")
  new.d <- data.frame(new.d, a3_18)
  new.d <- apply_labels(new.d, a3_18 = "Spanish")
  temp.d <- data.frame (new.d, a3_18)
  result<-questionr::freq(temp.d$a3_18, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "18. Spanish")
18. Spanish
n % val%
Spanish 1 0.4 100
NA 281 99.6 NA
Total 282 100.0 100 
a3_19 <- as.factor(d[,"a3_19"])
  levels(a3_19) <- list(Mexican="1")
  new.d <- data.frame(new.d, a3_19)
  new.d <- apply_labels(new.d, a3_19 = "Mexican")
  temp.d <- data.frame (new.d, a3_19)
  result<-questionr::freq(temp.d$a3_19, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "19. Mexican")
19. Mexican
n % val%
Mexican 0 0 NaN
NA 282 100 NA
Total 282 100 100 
a3_20 <- as.factor(d[,"a3_20"])
  levels(a3_20) <- list(Salvadoran="1")
  new.d <- data.frame(new.d, a3_20)
  new.d <- apply_labels(new.d, a3_20 = "Salvadoran")
  temp.d <- data.frame (new.d, a3_20)
  result<-questionr::freq(temp.d$a3_20, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "20. Salvadoran")
20. Salvadoran
n % val%
Salvadoran 0 0 NaN
NA 282 100 NA
Total 282 100 100 
a3_21 <- as.factor(d[,"a3_21"])
  levels(a3_21) <- list(Puerto_Rican="1")
  new.d <- data.frame(new.d, a3_21)
  new.d <- apply_labels(new.d, a3_21 = "Puerto_Rican")
  temp.d <- data.frame (new.d, a3_21)
  result<-questionr::freq(temp.d$a3_21, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "21. Puerto_Rican")
21. Puerto_Rican
n % val%
Puerto_Rican 0 0 NaN
NA 282 100 NA
Total 282 100 100 
a3_22 <- as.factor(d[,"a3_22"])
  levels(a3_22) <- list(Dominican="1")
  new.d <- data.frame(new.d, a3_22)
  new.d <- apply_labels(new.d, a3_22 = "Dominican")
  temp.d <- data.frame (new.d, a3_22)
  result<-questionr::freq(temp.d$a3_22, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "22. Dominican")
22. Dominican
n % val%
Dominican 0 0 NaN
NA 282 100 NA
Total 282 100 100 
a3_23 <- as.factor(d[,"a3_23"])
  levels(a3_23) <- list(Columbian="1")
  new.d <- data.frame(new.d, a3_23)
  new.d <- apply_labels(new.d, a3_23 = "Columbian")
  temp.d <- data.frame (new.d, a3_23)
  result<-questionr::freq(temp.d$a3_23, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "23. Columbian")
23. Columbian
n % val%
Columbian 0 0 NaN
NA 282 100 NA
Total 282 100 100 
a3_24 <- as.factor(d[,"a3_24"])
  levels(a3_23) <- list(Other="1")
  new.d <- data.frame(new.d, a3_24)
  new.d <- apply_labels(new.d, a3_24 = "Other")
  temp.d <- data.frame (new.d, a3_24)
  result<-questionr::freq(temp.d$a3_24, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "24. Other")
24. Other
n % val%
1 4 1.4 100
NA 278 98.6 NA
Total 282 100.0 100

A3 Other: Black or African American group

a3other <- d[,"a3other"]
  new.d <- data.frame(new.d, a3other)
  new.d <- apply_labels(new.d, a3other = "A3Other")
  temp.d <- data.frame (new.d, a3other)
result<-questionr::freq(temp.d$a3other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A3Other")
A3Other
n % val%
French 1 0.4 16.7
German/Jewish 1 0.4 16.7
Ghanaian 1 0.4 16.7
Moorish National Indigenous to 1 0.4 16.7
Panamanian 1 0.4 16.7
Rwandan 1 0.4 16.7
NA 276 97.9 NA
Total 282 100.0 100.0

A4: Month and year of birth

A4. What is your month and year of birth?

# a4month
a4month <- as.factor(d[,"a4month"])
  new.d <- data.frame(new.d, a4month)
  new.d <- apply_labels(new.d, a4month = "Month of birth")
  temp.d <- data.frame (new.d, a4month) 
  
  result<-questionr::freq(temp.d$a4month, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A4: Month of birth")
A4: Month of birth
n % val%
1 21 7.4 7.6
10 23 8.2 8.3
11 25 8.9 9.0
12 21 7.4 7.6
2 22 7.8 7.9
3 14 5.0 5.0
4 18 6.4 6.5
5 26 9.2 9.4
6 27 9.6 9.7
7 35 12.4 12.6
8 23 8.2 8.3
9 23 8.2 8.3
NA 4 1.4 NA
Total 282 100.0 100.0 
#a4year
a4year <- as.factor(d[,"a4year"])
  new.d <- data.frame(new.d, a4year)
  new.d <- apply_labels(new.d, a4year = "Year of birth")
  temp.d <- data.frame (new.d, a4year) 

  result<-questionr::freq(temp.d$a4year, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A4: Year of birth")
A4: Year of birth
n % val%
1941 5 1.8 1.8
1942 6 2.1 2.2
1943 6 2.1 2.2
1944 4 1.4 1.4
1945 11 3.9 4.0
1946 12 4.3 4.3
1947 10 3.5 3.6
1948 15 5.3 5.4
1949 20 7.1 7.2
1950 17 6.0 6.1
1951 14 5.0 5.0
1952 12 4.3 4.3
1953 15 5.3 5.4
1954 18 6.4 6.5
1955 15 5.3 5.4
1956 13 4.6 4.7
1957 10 3.5 3.6
1958 10 3.5 3.6
1959 9 3.2 3.2
1960 17 6.0 6.1
1961 11 3.9 4.0
1962 6 2.1 2.2
1963 8 2.8 2.9
1964 2 0.7 0.7
1965 6 2.1 2.2
1967 1 0.4 0.4
1968 1 0.4 0.4
1969 2 0.7 0.7
1971 1 0.4 0.4
748 1 0.4 0.4
NA 4 1.4 NA
Total 282 100.0 100.0

A5: Where were you born

  • A5. Where were you born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a5 <- as.factor(d[,"a5"])
# Make "*" to NA
a5[which(a5=="*")]<-"NA"
levels(a5) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a5 <- ordered(a5, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a5)
  new.d <- apply_labels(new.d, a5 = "Born place")
  temp.d <- data.frame (new.d, a5) 
  
  result<-questionr::freq(temp.d$a5, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A5: Where were you born?")
A5: Where were you born?
n % val%
US 245 86.9 88.4
Africa 22 7.8 7.9
Cuba_Caribbean 4 1.4 1.4
Other 6 2.1 2.2
NA 5 1.8 NA
Total 282 100.0 100.0

A5 Other: Where were you born

a5other <- d[,"a5other"]
  new.d <- data.frame(new.d, a5other)
  new.d <- apply_labels(new.d, a5other = "a5other")
  temp.d <- data.frame (new.d, a5other)
result<-questionr::freq(temp.d$a5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A5Other")
A5Other
n % val%
Belize, Central America 1 0.4 8.3
England 1 0.4 8.3
Germany 1 0.4 8.3
Ghana 1 0.4 8.3
Jackson, Miss. 1 0.4 8.3
Jamaica 1 0.4 8.3
Jamaica WI 1 0.4 8.3
Jamaican 1 0.4 8.3
Kientra, Morocco 1 0.4 8.3
San Diego, CA 1 0.4 8.3
United States Texas 1 0.4 8.3
Venezuela 1 0.4 8.3
NA 270 95.7 NA
Total 282 100.0 100.0

A6: Biological father born

  • A6. Where was your biological father born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a6 <- as.factor(d[,"a6"])
# Make "*" to NA
a6[which(a6=="*")]<-"NA"
levels(a6) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a6 <- ordered(a6, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a6)
  new.d <- apply_labels(new.d, a6 = "Born place")
  temp.d <- data.frame (new.d, a6) 
  
  result<-questionr::freq(temp.d$a6, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a6: Where were you born?")
a6: Where were you born?
n % val%
US 240 85.1 87.0
Africa 22 7.8 8.0
Cuba_Caribbean 7 2.5 2.5
Other 7 2.5 2.5
NA 6 2.1 NA
Total 282 100.0 100.0

A6 Other: Biological father born

a6other <- d[,"a6other"]
  new.d <- data.frame(new.d, a6other)
  new.d <- apply_labels(new.d, a6other = "a6other")
  temp.d <- data.frame (new.d, a6other)
result<-questionr::freq(temp.d$a6other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A6Other")
A6Other
n % val%
Belize, Central America 1 0.4 9.1
Blackstone, VA 1 0.4 9.1
Don’t know 1 0.4 9.1
England 1 0.4 9.1
Jamaica 1 0.4 9.1
Jamaica WI 1 0.4 9.1
Jamaican 1 0.4 9.1
Mississippi 1 0.4 9.1
Not known 1 0.4 9.1
Panama 1 0.4 9.1
United S Texas 1 0.4 9.1
NA 271 96.1 NA
Total 282 100.0 100.0

A7: Biological mother born

  • A7. Where was your biological mother born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a7 <- as.factor(d[,"a7"])
# Make "*" to NA
a7[which(a7=="*")]<-"NA"
levels(a7) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a7 <- ordered(a7, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a7)
  new.d <- apply_labels(new.d, a7 = "Born place")
  temp.d <- data.frame (new.d, a7) 
  
  result<-questionr::freq(temp.d$a7, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a7: Where were you born?")
a7: Where were you born?
n % val%
US 241 85.5 87.3
Africa 22 7.8 8.0
Cuba_Caribbean 6 2.1 2.2
Other 7 2.5 2.5
NA 6 2.1 NA
Total 282 100.0 100.0

A7 Other: Biological father born

a7other <- d[,"a7other"]
  new.d <- data.frame(new.d, a7other)
  new.d <- apply_labels(new.d, a7other = "a7other")
  temp.d <- data.frame (new.d, a7other)
result<-questionr::freq(temp.d$a7other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A7Other")
A7Other
n % val%
Belize, Central America 1 0.4 9.1
Bremont, TX 1 0.4 9.1
England 1 0.4 9.1
Jamaica WI 1 0.4 9.1
Japan 1 0.4 9.1
Mississippi 1 0.4 9.1
Oklahoma 1 0.4 9.1
Panama 1 0.4 9.1
Trinidad West Indies 1 0.4 9.1
United States Texas 1 0.4 9.1
Venezuela 1 0.4 9.1
NA 271 96.1 NA
Total 282 100.0 100.0

A8: Years lived in the US

  • A8. How many years have you lived in the United States?
    • 1=15 years or less
    • 2=16-25 years
    • 3=My whole life or more than 25 years
a8 <- as.factor(d[,"a8"])
# Make "*" to NA
a8[which(a8=="*")]<-"NA"
levels(a8) <- list(less_or_15="1",
                   years_16_25="2",
                   more_than_25_or_whole_life= "3")
  a8 <- ordered(a8, c("less_or_15","years_16_25","more_than_25_or_whole_life"))
  
  new.d <- data.frame(new.d, a8)
  new.d <- apply_labels(new.d, a8 = "Years lived in the US")
  temp.d <- data.frame (new.d, a8) 
  
  result<-questionr::freq(temp.d$a8, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A8")
A8
n % val%
less_or_15 6 2.1 2.2
years_16_25 4 1.4 1.5
more_than_25_or_whole_life 263 93.3 96.3
NA 9 3.2 NA
Total 282 100.0 100.0

B1A: Father

  • B1Aa: Father: Has this person had prostate cancer?
  • B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
  • B1Ac: Father: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Aa: Father: Has this person had prostate cancer?
  b1aa <- as.factor(d[,"b1aa"])
# Make "*" to NA
b1aa[which(b1aa=="*")]<-"NA"
  levels(b1aa) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1aa <- ordered(b1aa, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1aa)
  new.d <- apply_labels(new.d, b1aa = "Father")
  temp.d <- data.frame (new.d, b1aa)  
  
  result<-questionr::freq(temp.d$b1aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Aa: Father: Has this person had prostate cancer?")
B1Aa: Father: Has this person had prostate cancer?
n % val%
No 147 52.1 54.9
Yes 54 19.1 20.1
Dont_know 67 23.8 25.0
NA 14 5.0 NA
Total 282 100.0 100.0 
#B1Ab: Father: Was he (or any) diagnosed BEFORE age 55? 
  b1ab <- as.factor(d[,"b1ab"])
# Make "*" to NA
b1ab[which(b1ab=="*")]<-"NA"
  levels(b1ab) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ab <- ordered(b1ab, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ab)
  new.d <- apply_labels(new.d, b1ab = "Father")
  temp.d <- data.frame (new.d, b1ab)  
  
  result<-questionr::freq(temp.d$b1ab,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?")
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 63 22.3 62.4
Yes 6 2.1 5.9
Dont_know 32 11.3 31.7
NA 181 64.2 NA
Total 282 100.0 100.0 
#B1Ac: Father: Did he (or any) die of prostate cancer?
  b1ac <- as.factor(d[,"b1ac"])
  # Make "*" to NA
b1ac[which(b1ac=="*")]<-"NA"
  levels(b1ac) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ac <- ordered(b1ac, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ac)
  new.d <- apply_labels(new.d, b1ac = "Father")
  temp.d <- data.frame (new.d, b1ac)  
  
  result<-questionr::freq(temp.d$b1ac,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ac: Father: Did he (or any) die of prostate cancer?")
B1Ac: Father: Did he (or any) die of prostate cancer?
n % val%
No 68 24.1 65.4
Yes 25 8.9 24.0
Dont_know 11 3.9 10.6
NA 178 63.1 NA
Total 282 100.0 100.0

B1B: Any Brother

  • B1BNo: Any Brother
    • 1=I had no brothers
    • if not marked
  • B1Ba: Any Brother: Has this person had prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Ba2: Any Brother: If Yes, number with prostate cancer
    • 1=1
    • 2=2+
  • B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Bc: Any Brother: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1BNo: Any Brother
  b1bno <- as.factor(d[,"b1bno"])
  levels(b1bno) <- list(No_brothers="1")

  new.d <- data.frame(new.d, b1bno)
  new.d <- apply_labels(new.d, b1bno = "Any Brother")
  temp.d <- data.frame (new.d, b1bno)  
  
  result<-questionr::freq(temp.d$b1bno,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1BNo: Any Brother")
B1BNo: Any Brother
n % val%
No_brothers 25 8.9 100
NA 257 91.1 NA
Total 282 100.0 100 
#B1Ba: Any Brother: Has this person had prostate cancer? 
  b1ba <- as.factor(d[,"b1ba"])
# Make "*" to NA
b1ba[which(b1ba=="*")]<-"NA"
  levels(b1ba) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ba <- ordered(b1ba, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ba)
  new.d <- apply_labels(new.d, b1ba = "Any Brother: have p cancer")
  temp.d <- data.frame (new.d, b1ba)  
  
  result<-questionr::freq(temp.d$b1ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ba: Any Brother: Has this person had prostate cancer?")
B1Ba: Any Brother: Has this person had prostate cancer?
n % val%
No 159 56.4 63.6
Yes 66 23.4 26.4
Dont_know 25 8.9 10.0
NA 32 11.3 NA
Total 282 100.0 100.0 
#B1Ba2: Any Brother: If Yes, number with prostate cancer
  b1ba2 <- as.factor(d[,"b1ba2"])
# Make "*" to NA
b1ba2[which(b1ba2=="*")]<-"NA"
  levels(b1ba2) <- list(One="1",
                     Two_or_more="2")
  b1ba2 <- ordered(b1ba2, c("One","Two_or_more"))
  
  new.d <- data.frame(new.d, b1ba2)
  new.d <- apply_labels(new.d, b1ba2 = "Number of brother")
  temp.d <- data.frame (new.d, b1ba2)  
  
  result<-questionr::freq(temp.d$b1ba2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ba2: Any Brother: If Yes, number with prostate cancer")
B1Ba2: Any Brother: If Yes, number with prostate cancer
n % val%
One 28 9.9 63.6
Two_or_more 16 5.7 36.4
NA 238 84.4 NA
Total 282 100.0 100.0 
#B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
  b1bb <- as.factor(d[,"b1bb"])
# Make "*" to NA
b1bb[which(b1bb=="*")]<-"NA"
  levels(b1bb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1bb <- ordered(b1bb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1bb)
  new.d <- apply_labels(new.d, b1bb = "Any Brother: before 55")
  temp.d <- data.frame (new.d, b1bb)  
  
  result<-questionr::freq(temp.d$b1bb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?")
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 65 23.0 63.7
Yes 20 7.1 19.6
Dont_know 17 6.0 16.7
NA 180 63.8 NA
Total 282 100.0 100.0 
#B1Bc: Any Brother: Did he (or any) die of prostate cancer?
  b1bc <- as.factor(d[,"b1bc"])
  # Make "*" to NA
b1bc[which(b1bc=="*")]<-"NA"
  levels(b1bc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1bc <- ordered(b1bc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1bc)
  new.d <- apply_labels(new.d, b1bc = "Any Brother: die")
  temp.d <- data.frame (new.d, b1bc)  
  
  result<-questionr::freq(temp.d$b1bc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Bc: Any Brother: Did he (or any) die of prostate cancer?")
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
n % val%
No 79 28.0 82.3
Yes 11 3.9 11.5
Dont_know 6 2.1 6.2
NA 186 66.0 NA
Total 282 100.0 100.0

B1C: Any Son

  • B1CNo: Any Son
    • 1=I had no sons
    • if not marked
  • B1Ca: Any Son: Has this person had prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Ca2: Any Son: If Yes, number with prostate cancer
    • 1=1
    • 2=2+
  • B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Cc: Any Son: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1BNo
  b1cno <- as.factor(d[,"b1cno"])
  levels(b1cno) <- list(No_brothers="1")

  new.d <- data.frame(new.d, b1cno)
  new.d <- apply_labels(new.d, b1cno = "Any Son")
  temp.d <- data.frame (new.d, b1cno)  
  
  result<-questionr::freq(temp.d$b1cno,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1CNo: Any Son")
B1CNo: Any Son
n % val%
No_brothers 52 18.4 100
NA 230 81.6 NA
Total 282 100.0 100 
#B1Ca
  b1ca <- as.factor(d[,"b1ca"])
  # Make "*" to NA
b1ca[which(b1ca=="*")]<-"NA"
  levels(b1ca) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ca <- ordered(b1ca, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ca)
  new.d <- apply_labels(new.d, b1ca = "Any Son: have p cancer")
  temp.d <- data.frame (new.d, b1ca)  
  
  result<-questionr::freq(temp.d$b1ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ca: Any Son: Has this person had prostate cancer?")
B1Ca: Any Son: Has this person had prostate cancer?
n % val%
No 209 74.1 92.1
Yes 9 3.2 4.0
Dont_know 9 3.2 4.0
NA 55 19.5 NA
Total 282 100.0 100.0 
#B1Ca2
  b1ca2 <- as.factor(d[,"b1ca2"])
  # Make "*" to NA
b1ca2[which(b1ca2=="*")]<-"NA"
  levels(b1ca2) <- list(One="1",
                     Two_or_more="2")
  b1ca2 <- ordered(b1ca2, c("One","Two_or_more"))
  
  new.d <- data.frame(new.d, b1ca2)
  new.d <- apply_labels(new.d, b1ca2 = "Number of sons")
  temp.d <- data.frame (new.d, b1ca2)  
  
  result<-questionr::freq(temp.d$b1ca2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ca2: Any Son: If Yes, number with prostate cancer")
B1Ca2: Any Son: If Yes, number with prostate cancer
n % val%
One 2 0.7 50
Two_or_more 2 0.7 50
NA 278 98.6 NA
Total 282 100.0 100 
#B1Cb
  b1cb <- as.factor(d[,"b1cb"])
  # Make "*" to NA
b1cb[which(b1cb=="*")]<-"NA"
  levels(b1cb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1cb <- ordered(b1cb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1cb)
  new.d <- apply_labels(new.d, b1cb = "Any Son: before 55")
  temp.d <- data.frame (new.d, b1cb)  
  
  result<-questionr::freq(temp.d$b1cb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?")
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 51 18.1 92.7
Yes 0 0.0 0.0
Dont_know 4 1.4 7.3
NA 227 80.5 NA
Total 282 100.0 100.0 
#B1Cc
  b1cc <- as.factor(d[,"b1cc"])
  # Make "*" to NA
b1cc[which(b1cc=="*")]<-"NA"
  levels(b1cc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1cc <- ordered(b1cc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1cc)
  new.d <- apply_labels(new.d, b1cc = "Any Son: die")
  temp.d <- data.frame (new.d, b1cc)  
  
  result<-questionr::freq(temp.d$b1cc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Cc: Any Son: Did he (or any) die of prostate cancer?")
B1Cc: Any Son: Did he (or any) die of prostate cancer?
n % val%
No 50 17.7 94.3
Yes 0 0.0 0.0
Dont_know 3 1.1 5.7
NA 229 81.2 NA
Total 282 100.0 100.0

B1D: Maternal Grandfather

  • B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
  • B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
  • b1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
  b1da <- as.factor(d[,"b1da"])
# Make "*" to NA
b1da[which(b1da=="*")]<-"NA"
  levels(b1da) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1da <- ordered(b1da, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1da)
  new.d <- apply_labels(new.d, b1da = "Father")
  temp.d <- data.frame (new.d, b1da)  
  
  result<-questionr::freq(temp.d$b1da,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?")
B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
n % val%
No 118 41.8 44.4
Yes 9 3.2 3.4
Dont_know 139 49.3 52.3
NA 16 5.7 NA
Total 282 100.0 100.0 
# B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
  b1db <- as.factor(d[,"b1db"])
  # Make "*" to NA
b1db[which(b1db=="*")]<-"NA"
  levels(b1db) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1db <- ordered(b1db, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1db)
  new.d <- apply_labels(new.d, b1db = "Father")
  temp.d <- data.frame (new.d, b1db)  
  
  result<-questionr::freq(temp.d$b1db,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 31 11.0 42.5
Yes 0 0.0 0.0
Dont_know 42 14.9 57.5
NA 209 74.1 NA
Total 282 100.0 100.0 
# B1Dc: Maternal Grandfather (Mom’s  side): Did he (or any) die of prostate cancer?
  b1dc <- as.factor(d[,"b1dc"])
  # Make "*" to NA
b1dc[which(b1dc=="*")]<-"NA"
  levels(b1dc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1dc <- ordered(b1dc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1dc)
  new.d <- apply_labels(new.d, b1dc = "Father")
  temp.d <- data.frame (new.d, b1dc)  
  
  result<-questionr::freq(temp.d$b1dc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Dc: Maternal Grandfather (Mom’s  side): Did he (or any) die of prostate cancer?")
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
n % val%
No 32 11.3 44.4
Yes 6 2.1 8.3
Dont_know 34 12.1 47.2
NA 210 74.5 NA
Total 282 100.0 100.0

B1E: Paternal Grandfather

  • B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
  • B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
  • B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer? 
  b1ea <- as.factor(d[,"b1ea"])
# Make "*" to NA
b1ea[which(b1ea=="*")]<-"NA"
  levels(b1ea) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ea <- ordered(b1ea, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ea)
  new.d <- apply_labels(new.d, b1ea = "Father")
  temp.d <- data.frame (new.d, b1ea)  
  
  result<-questionr::freq(temp.d$b1ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?")
B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
n % val%
No 103 36.5 39.0
Yes 6 2.1 2.3
Dont_know 155 55.0 58.7
NA 18 6.4 NA
Total 282 100.0 100.0 
# B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
  b1eb <- as.factor(d[,"b1eb"])
  # Make "*" to NA
b1eb[which(b1eb=="*")]<-"NA"
  levels(b1eb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1eb <- ordered(b1eb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1eb)
  new.d <- apply_labels(new.d, b1eb = "Father")
  temp.d <- data.frame (new.d, b1eb)  
  
  result<-questionr::freq(temp.d$b1eb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 25 8.9 36.8
Yes 0 0.0 0.0
Dont_know 43 15.2 63.2
NA 214 75.9 NA
Total 282 100.0 100.0 
# B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
  b1ec <- as.factor(d[,"b1ec"])
  # Make "*" to NA
b1ec[which(b1ec=="*")]<-"NA"
  levels(b1ec) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ec <- ordered(b1ec, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ec)
  new.d <- apply_labels(new.d, b1ec = "Father")
  temp.d <- data.frame (new.d, b1ec)  
  
  result<-questionr::freq(temp.d$b1ec,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?")
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
n % val%
No 24 8.5 34.8
Yes 2 0.7 2.9
Dont_know 43 15.2 62.3
NA 213 75.5 NA
Total 282 100.0 100.0

B2: Family History (Other cancers)

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)?
    • 2=Yes
    • 1=No
b2 <- as.factor(d[,"b2"])
# Make "*" to NA
b2[which(b2=="*")]<-"NA"
levels(b2) <- list(No="1",
                   Yes="2")
  b2 <- ordered(b2, c("Yes","No"))
  
  new.d <- data.frame(new.d, b2)
  new.d <- apply_labels(new.d, b2 = "Month Diagnosed")
  temp.d <- data.frame (new.d, b2) 
  
  result<-questionr::freq(temp.d$b2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B2")
B2
n % val%
Yes 55 19.5 31.8
No 118 41.8 68.2
NA 109 38.7 NA
Total 282 100.0 100.0

B2A: Mother

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2A_1: 1=Breast
    • B2A_2: 1=Ovarian
    • B2A_3: 1=Colorectal
    • B2A_4: 1=Lung
    • B2A_5: 1=Other Cancer
  b2a_1 <- as.factor(d[,"b2a_1"])
  levels(b2a_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2a_1)
  new.d <- apply_labels(new.d, b2a_1 = "Breast")
  temp.d <- data.frame (new.d, b2a_1)  
  result<-questionr::freq(temp.d$b2a_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 21 7.4 100
NA 261 92.6 NA
Total 282 100.0 100 
  b2a_2 <- as.factor(d[,"b2a_2"])
  levels(b2a_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2a_2)
  new.d <- apply_labels(new.d, b2a_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2a_2)  
  result<-questionr::freq(temp.d$b2a_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 9 3.2 100
NA 273 96.8 NA
Total 282 100.0 100 
  b2a_3 <- as.factor(d[,"b2a_3"])
  levels(b2a_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2a_3)
  new.d <- apply_labels(new.d, b2a_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2a_3)  
  
  result<-questionr::freq(temp.d$b2a_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 6 2.1 100
NA 276 97.9 NA
Total 282 100.0 100 
  b2a_4 <- as.factor(d[,"b2a_4"])
  levels(b2a_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2a_4)
  new.d <- apply_labels(new.d, b2a_4 = "Lung")
  temp.d <- data.frame (new.d, b2a_4)  
  
  result<-questionr::freq(temp.d$b2a_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 11 3.9 100
NA 271 96.1 NA
Total 282 100.0 100 
  b2a_5 <- as.factor(d[,"b2a_5"])
  levels(b2a_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2a_5)
  new.d <- apply_labels(new.d, b2a_5 = "Lung")
  temp.d <- data.frame (new.d, b2a_5)  
  
  result<-questionr::freq(temp.d$b2a_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 15 5.3 100
NA 267 94.7 NA
Total 282 100.0 100

B2B: Father

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2B_1: 1=Breast
    • B2B_3: 1=Colorectal
    • B2B_4: 1=Lung
    • B2B_5: 1=Other Cancer
  b2b_1 <- as.factor(d[,"b2b_1"])
  levels(b2b_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2b_1)
  new.d <- apply_labels(new.d, b2b_1 = "Breast")
  temp.d <- data.frame (new.d, b2b_1)  
  result<-questionr::freq(temp.d$b2b_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 4 1.4 100
NA 278 98.6 NA
Total 282 100.0 100 
  b2b_3 <- as.factor(d[,"b2b_3"])
  levels(b2b_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2b_3)
  new.d <- apply_labels(new.d, b2b_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2b_3)  
  
  result<-questionr::freq(temp.d$b2b_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 7 2.5 100
NA 275 97.5 NA
Total 282 100.0 100 
  b2b_4 <- as.factor(d[,"b2b_4"])
  levels(b2b_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2b_4)
  new.d <- apply_labels(new.d, b2b_4 = "Lung")
  temp.d <- data.frame (new.d, b2b_4)  
  
  result<-questionr::freq(temp.d$b2b_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 13 4.6 100
NA 269 95.4 NA
Total 282 100.0 100 
  b2b_5 <- as.factor(d[,"b2b_5"])
  levels(b2b_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2b_5)
  new.d <- apply_labels(new.d, b2b_5 = "Lung")
  temp.d <- data.frame (new.d, b2b_5)  
  
  result<-questionr::freq(temp.d$b2b_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 21 7.4 100
NA 261 92.6 NA
Total 282 100.0 100

B2C: Any sister

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2C_1: 1=Breast
    • B2C_2: 1=Ovarian
    • B2C_3: 1=Colorectal
    • B2C_4: 1=Lung
    • B2C_5: 1=Other Cancer
  b2c_1 <- as.factor(d[,"b2c_1"])
  levels(b2c_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2c_1)
  new.d <- apply_labels(new.d, b2c_1 = "Breast")
  temp.d <- data.frame (new.d, b2c_1)  
  result<-questionr::freq(temp.d$b2c_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 33 11.7 100
NA 249 88.3 NA
Total 282 100.0 100 
  b2c_2 <- as.factor(d[,"b2c_2"])
  levels(b2c_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2c_2)
  new.d <- apply_labels(new.d, b2c_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2c_2)  
  result<-questionr::freq(temp.d$b2c_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 6 2.1 100
NA 276 97.9 NA
Total 282 100.0 100 
  b2c_3 <- as.factor(d[,"b2c_3"])
  levels(b2c_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2c_3)
  new.d <- apply_labels(new.d, b2c_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2c_3)  
  
  result<-questionr::freq(temp.d$b2c_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 2 0.7 100
NA 280 99.3 NA
Total 282 100.0 100 
  b2c_4 <- as.factor(d[,"b2c_4"])
  levels(b2c_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2c_4)
  new.d <- apply_labels(new.d, b2c_4 = "Lung")
  temp.d <- data.frame (new.d, b2c_4)  
  
  result<-questionr::freq(temp.d$b2c_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 5 1.8 100
NA 277 98.2 NA
Total 282 100.0 100 
  b2c_5 <- as.factor(d[,"b2c_5"])
  levels(b2c_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2c_5)
  new.d <- apply_labels(new.d, b2c_5 = "Lung")
  temp.d <- data.frame (new.d, b2c_5)  
  
  result<-questionr::freq(temp.d$b2c_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 11 3.9 100
NA 271 96.1 NA
Total 282 100.0 100

B2D: Any brother

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2D_1: 1=Breast
    • B2D_3: 1=Colorectal
    • B2D_4: 1=Lung
    • B2D_5: 1=Other Cancer
  b2d_1 <- as.factor(d[,"b2d_1"])
  levels(b2d_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2d_1)
  new.d <- apply_labels(new.d, b2d_1 = "Breast")
  temp.d <- data.frame (new.d, b2d_1)  
  result<-questionr::freq(temp.d$b2d_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 2 0.7 100
NA 280 99.3 NA
Total 282 100.0 100 
  b2d_3 <- as.factor(d[,"b2d_3"])
  levels(b2d_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2d_3)
  new.d <- apply_labels(new.d, b2d_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2d_3)  
  
  result<-questionr::freq(temp.d$b2d_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 4 1.4 100
NA 278 98.6 NA
Total 282 100.0 100 
  b2d_4 <- as.factor(d[,"b2d_4"])
  levels(b2d_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2d_4)
  new.d <- apply_labels(new.d, b2d_4 = "Lung")
  temp.d <- data.frame (new.d, b2d_4)  
  
  result<-questionr::freq(temp.d$b2d_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 10 3.5 100
NA 272 96.5 NA
Total 282 100.0 100 
  b2d_5 <- as.factor(d[,"b2d_5"])
  levels(b2d_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2d_5)
  new.d <- apply_labels(new.d, b2d_5 = "Lung")
  temp.d <- data.frame (new.d, b2d_5)  
  
  result<-questionr::freq(temp.d$b2d_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 16 5.7 100
NA 266 94.3 NA
Total 282 100.0 100

B2E: Any daughter

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2E_1: 1=Breast
    • B2E_2: 1=Ovarian
    • B2E_3: 1=Colorectal
    • B2E_4: 1=Lung
    • B2E_5: 1=Other Cancer
  b2e_1 <- as.factor(d[,"b2e_1"])
  levels(b2e_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2e_1)
  new.d <- apply_labels(new.d, b2e_1 = "Breast")
  temp.d <- data.frame (new.d, b2e_1)  
  result<-questionr::freq(temp.d$b2e_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 4 1.4 100
NA 278 98.6 NA
Total 282 100.0 100 
  b2e_2 <- as.factor(d[,"b2e_2"])
  levels(b2e_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2e_2)
  new.d <- apply_labels(new.d, b2e_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2e_2)  
  result<-questionr::freq(temp.d$b2e_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 4 1.4 100
NA 278 98.6 NA
Total 282 100.0 100 
  b2e_3 <- as.factor(d[,"b2e_3"])
  levels(b2e_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2e_3)
  new.d <- apply_labels(new.d, b2e_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2e_3)  
  
  result<-questionr::freq(temp.d$b2e_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 0 0 NaN
NA 282 100 NA
Total 282 100 100 
  b2e_4 <- as.factor(d[,"b2e_4"])
  levels(b2e_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2e_4)
  new.d <- apply_labels(new.d, b2e_4 = "Lung")
  temp.d <- data.frame (new.d, b2e_4)  
  
  result<-questionr::freq(temp.d$b2e_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 1 0.4 100
NA 281 99.6 NA
Total 282 100.0 100 
  b2e_5 <- as.factor(d[,"b2e_5"])
  levels(b2e_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2e_5)
  new.d <- apply_labels(new.d, b2e_5 = "Lung")
  temp.d <- data.frame (new.d, b2e_5)  
  
  result<-questionr::freq(temp.d$b2e_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 0 0 NaN
NA 282 100 NA
Total 282 100 100

B2F: Any son

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2F_1: 1=Breast
    • B2F_3: 1=Colorectal
    • B2F_4: 1=Lung
    • B2F_5: 1=Other Cancer
  b2f_1 <- as.factor(d[,"b2f_1"])
  levels(b2f_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2f_1)
  new.d <- apply_labels(new.d, b2f_1 = "Breast")
  temp.d <- data.frame (new.d, b2f_1)  
  result<-questionr::freq(temp.d$b2f_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 0 0 NaN
NA 282 100 NA
Total 282 100 100 
  b2f_3 <- as.factor(d[,"b2f_3"])
  levels(b2f_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2f_3)
  new.d <- apply_labels(new.d, b2f_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2f_3)  
  
  result<-questionr::freq(temp.d$b2f_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 0 0 NaN
NA 282 100 NA
Total 282 100 100 
  b2f_4 <- as.factor(d[,"b2f_4"])
  levels(b2f_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2f_4)
  new.d <- apply_labels(new.d, b2f_4 = "Lung")
  temp.d <- data.frame (new.d, b2f_4)  
  
  result<-questionr::freq(temp.d$b2f_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 0 0 NaN
NA 282 100 NA
Total 282 100 100 
  b2f_5 <- as.factor(d[,"b2f_5"])
  levels(b2f_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2f_5)
  new.d <- apply_labels(new.d, b2f_5 = "Lung")
  temp.d <- data.frame (new.d, b2f_5)  
  
  result<-questionr::freq(temp.d$b2f_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 1 0.4 100
NA 281 99.6 NA
Total 282 100.0 100

B3: Current health

  • B3. In general, how would you rate your current health?
    • 1=Excellent
    • 2=Very Good
    • 3=Good
    • 4=Fair
    • 5=Poor
  b3 <- as.factor(d[,"b3"])
# Make "*" to NA
b3[which(b3=="*")]<-"NA"
  levels(b3) <- list(Excellent="1",
                     Very_Good="2",
                     Good="3",
                     Fair="4",
                     Poor="5")
  b3 <- ordered(b3, c("Excellent","Very_Good","Good","Fair","Poor"))

  new.d <- data.frame(new.d, b3)
  new.d <- apply_labels(new.d, b3 = "Current Health")
  temp.d <- data.frame (new.d, b3)  
  
  result<-questionr::freq(temp.d$b3, cum = TRUE, total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val% %cum val%cum
Excellent 20 7.1 7.5 7.1 7.5
Very_Good 75 26.6 28.1 33.7 35.6
Good 106 37.6 39.7 71.3 75.3
Fair 58 20.6 21.7 91.8 97.0
Poor 8 2.8 3.0 94.7 100.0
NA 15 5.3 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

B4: Comorbidities

  • B4. Has the doctor ever told you that you have/had…
    • Heart Attack
    • Heart Failure or CHF
    • Stroke
    • Hypertension
    • Peripheral arterial disease
    • High Cholesterol
    • Asthma, COPD
    • Stomach ulcers
    • Crohn’s Disease
    • Diabetes
    • Kidney Problems
    • Cirrhosis, liver damage
    • Arthritis
    • Dementia
    • Depression
    • AIDS
    • Other Cancer
# Heart Attack
  b4aa <- as.factor(d[,"b4aa"])
# Make "*" to NA
b4aa[which(b4aa=="*")]<-"NA"
  levels(b4aa) <- list(No="1",
                     Yes="2")
  b4aa <- ordered(b4aa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4aa)
  new.d <- apply_labels(new.d, b4aa = "Heart Attack")
  temp.d <- data.frame (new.d, b4aa)  
  
  result<-questionr::freq(temp.d$b4aa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Attack")
Heart Attack
n % val%
No 247 87.6 92.5
Yes 20 7.1 7.5
NA 15 5.3 NA
Total 282 100.0 100.0 
  b4ab <- as.factor(d[,"b4ab"])
  new.d <- data.frame(new.d, b4ab)
  new.d <- apply_labels(new.d, b4ab = "Heart Attack age")
  temp.d <- data.frame (new.d, b4ab)  
  result<-questionr::freq(temp.d$b4ab, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Attack Age")
Heart Attack Age
n % val%
29 1 0.4 5
48 1 0.4 5
49 1 0.4 5
50 3 1.1 15
52 1 0.4 5
53 1 0.4 5
55 2 0.7 10
57 1 0.4 5
58 3 1.1 15
59 2 0.7 10
61 1 0.4 5
69 1 0.4 5
74 2 0.7 10
NA 262 92.9 NA
Total 282 100.0 100 
# Heart Failure or CHF
  b4ba <- as.factor(d[,"b4ba"])
  # Make "*" to NA
b4ba[which(b4ba=="*")]<-"NA"
  levels(b4ba) <- list(No="1",
                     Yes="2")
  b4ba <- ordered(b4ba, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ba)
  new.d <- apply_labels(new.d, b4ba = "Heart Failure or CHF")
  temp.d <- data.frame (new.d, b4ba)  
  
  result<-questionr::freq(temp.d$b4ba, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF")
Heart Failure or CHF
n % val%
No 246 87.2 93.5
Yes 17 6.0 6.5
NA 19 6.7 NA
Total 282 100.0 100.0 
  b4bb <- as.factor(d[,"b4bb"])
  new.d <- data.frame(new.d, b4bb)
  new.d <- apply_labels(new.d, b4bb = "Heart Failure or CHF age")
  temp.d <- data.frame (new.d, b4bb)  
  result<-questionr::freq(temp.d$b4bb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF Age")
Heart Failure or CHF Age
n % val%
29 1 0.4 5.3
40 1 0.4 5.3
49 1 0.4 5.3
50 2 0.7 10.5
53 1 0.4 5.3
54 1 0.4 5.3
56 3 1.1 15.8
59 1 0.4 5.3
61 2 0.7 10.5
62 1 0.4 5.3
63 1 0.4 5.3
64 1 0.4 5.3
70 1 0.4 5.3
71 1 0.4 5.3
74 1 0.4 5.3
NA 263 93.3 NA
Total 282 100.0 100.0 
# Stroke  
  b4ca <- as.factor(d[,"b4ca"])
  # Make "*" to NA
b4ca[which(b4ca=="*")]<-"NA"
  levels(b4ca) <- list(No="1",
                     Yes="2")
  b4ca <- ordered(b4ca, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ca)
  new.d <- apply_labels(new.d, b4ca = "Stroke")
  temp.d <- data.frame (new.d, b4ca)  
  
  result<-questionr::freq(temp.d$b4ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stroke")
Stroke
n % val%
No 241 85.5 91.3
Yes 23 8.2 8.7
NA 18 6.4 NA
Total 282 100.0 100.0 
  b4cb <- as.factor(d[,"b4cb"])
  new.d <- data.frame(new.d, b4cb)
  new.d <- apply_labels(new.d, b4cb = "Stroke age")
  temp.d <- data.frame (new.d, b4cb)  
  result<-questionr::freq(temp.d$b4cb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stroke Age")
Stroke Age
n % val%
50 1 0.4 4.5
52 1 0.4 4.5
54 1 0.4 4.5
55 1 0.4 4.5
57 2 0.7 9.1
58 1 0.4 4.5
60 1 0.4 4.5
61 2 0.7 9.1
62 1 0.4 4.5
63 2 0.7 9.1
64 2 0.7 9.1
65 2 0.7 9.1
66 1 0.4 4.5
67 1 0.4 4.5
68 1 0.4 4.5
73 1 0.4 4.5
74 1 0.4 4.5
NA 260 92.2 NA
Total 282 100.0 100.0 
# Hypertension 
  b4da <- as.factor(d[,"b4da"])
# Make "*" to NA
b4da[which(b4da=="*")]<-"NA"
  levels(b4da) <- list(No="1",
                     Yes="2")
  b4da <- ordered(b4da, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4da)
  new.d <- apply_labels(new.d, b4da = "Hypertension")
  temp.d <- data.frame (new.d, b4da)  
  
  result<-questionr::freq(temp.d$b4da, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Hypertension")
Hypertension
n % val%
No 76 27.0 28.6
Yes 190 67.4 71.4
NA 16 5.7 NA
Total 282 100.0 100.0 
  b4db <- as.factor(d[,"b4db"])
  new.d <- data.frame(new.d, b4db)
  new.d <- apply_labels(new.d, b4db = "Hypertension age")
  temp.d <- data.frame (new.d, b4db)  
  result<-questionr::freq(temp.d$b4db, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Hypertension Age")
Hypertension Age
n % val%
21 2 0.7 1.1
22 2 0.7 1.1
25 1 0.4 0.5
26 1 0.4 0.5
27 1 0.4 0.5
28 1 0.4 0.5
29 2 0.7 1.1
30 3 1.1 1.6
33 2 0.7 1.1
35 4 1.4 2.2
36 4 1.4 2.2
38 1 0.4 0.5
39 2 0.7 1.1
40 11 3.9 6.0
41 1 0.4 0.5
44 1 0.4 0.5
45 17 6.0 9.3
46 3 1.1 1.6
47 2 0.7 1.1
48 2 0.7 1.1
49 3 1.1 1.6
50 25 8.9 13.7
52 1 0.4 0.5
53 1 0.4 0.5
54 7 2.5 3.8
55 27 9.6 14.8
56 3 1.1 1.6
57 2 0.7 1.1
58 4 1.4 2.2
59 3 1.1 1.6
6 1 0.4 0.5
60 11 3.9 6.0
61 5 1.8 2.7
62 6 2.1 3.3
63 1 0.4 0.5
64 2 0.7 1.1
65 2 0.7 1.1
66 2 0.7 1.1
68 3 1.1 1.6
69 2 0.7 1.1
70 1 0.4 0.5
71 1 0.4 0.5
72 1 0.4 0.5
73 1 0.4 0.5
75 1 0.4 0.5
78 1 0.4 0.5
89 1 0.4 0.5
99 2 0.7 1.1
NA 99 35.1 NA
Total 282 100.0 100.0 
# Peripheral arterial disease 
  b4ea <- as.factor(d[,"b4ea"])
# Make "*" to NA
b4ea[which(b4ea=="*")]<-"NA"  
  levels(b4ea) <- list(No="1",
                     Yes="2")
  b4ea <- ordered(b4ea, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ea)
  new.d <- apply_labels(new.d, b4ea = "Peripheral arterial disease")
  temp.d <- data.frame (new.d, b4ea)  
  
  result<-questionr::freq(temp.d$b4ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease")
Peripheral arterial disease
n % val%
No 237 84.0 92.9
Yes 18 6.4 7.1
NA 27 9.6 NA
Total 282 100.0 100.0 
  b4eb <- as.factor(d[,"b4eb"])
  new.d <- data.frame(new.d, b4eb)
  new.d <- apply_labels(new.d, b4eb = "Peripheral arterial disease age")
  temp.d <- data.frame (new.d, b4eb)  
  result<-questionr::freq(temp.d$b4eb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease Age")
Peripheral arterial disease Age
n % val%
25 1 0.4 4.5
36 1 0.4 4.5
45 2 0.7 9.1
46 1 0.4 4.5
48 2 0.7 9.1
50 2 0.7 9.1
55 1 0.4 4.5
56 1 0.4 4.5
58 1 0.4 4.5
60 1 0.4 4.5
62 1 0.4 4.5
66 1 0.4 4.5
68 3 1.1 13.6
69 1 0.4 4.5
71 1 0.4 4.5
73 1 0.4 4.5
74 1 0.4 4.5
NA 260 92.2 NA
Total 282 100.0 100.0 
# High Cholesterol 
  b4fa <- as.factor(d[,"b4fa"])
  # Make "*" to NA
b4fa[which(b4fa=="*")]<-"NA"
  levels(b4fa) <- list(No="1",
                     Yes="2")
  b4fa <- ordered(b4fa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4fa)
  new.d <- apply_labels(new.d, b4fa = "High Cholesterol")
  temp.d <- data.frame (new.d, b4fa)  
  
  result<-questionr::freq(temp.d$b4fa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "High Cholesterol")
High Cholesterol
n % val%
No 132 46.8 50.2
Yes 131 46.5 49.8
NA 19 6.7 NA
Total 282 100.0 100.0 
  b4fb <- as.factor(d[,"b4fb"])
  new.d <- data.frame(new.d, b4fb)
  new.d <- apply_labels(new.d, b4fb = "High Cholesterol age")
  temp.d <- data.frame (new.d, b4fb)  
  result<-questionr::freq(temp.d$b4fb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "High Cholesterol Age")
High Cholesterol Age
n % val%
12 1 0.4 0.8
19 1 0.4 0.8
26 2 0.7 1.7
28 1 0.4 0.8
29 1 0.4 0.8
36 3 1.1 2.5
39 1 0.4 0.8
40 7 2.5 5.8
42 2 0.7 1.7
44 2 0.7 1.7
45 13 4.6 10.7
46 2 0.7 1.7
47 4 1.4 3.3
48 2 0.7 1.7
49 1 0.4 0.8
5 1 0.4 0.8
50 14 5.0 11.6
51 1 0.4 0.8
53 3 1.1 2.5
54 4 1.4 3.3
55 10 3.5 8.3
56 3 1.1 2.5
57 1 0.4 0.8
58 3 1.1 2.5
59 2 0.7 1.7
6 1 0.4 0.8
60 10 3.5 8.3
61 2 0.7 1.7
62 4 1.4 3.3
63 1 0.4 0.8
64 3 1.1 2.5
65 5 1.8 4.1
67 2 0.7 1.7
68 1 0.4 0.8
69 2 0.7 1.7
70 2 0.7 1.7
72 1 0.4 0.8
75 1 0.4 0.8
92 1 0.4 0.8
NA 161 57.1 NA
Total 282 100.0 100.0 
#  Asthma, COPD
  b4ga <- as.factor(d[,"b4ga"])
  # Make "*" to NA
b4ga[which(b4ga=="*")]<-"NA"
  levels(b4ga) <- list(No="1",
                     Yes="2")
  b4ga <- ordered(b4ga, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ga)
  new.d <- apply_labels(new.d, b4ga = "Asthma, COPD")
  temp.d <- data.frame (new.d, b4ga)  
  
  result<-questionr::freq(temp.d$b4ga, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Asthma, COPD")
Asthma, COPD
n % val%
No 230 81.6 83.9
Yes 44 15.6 16.1
NA 8 2.8 NA
Total 282 100.0 100.0 
  b4gb <- as.factor(d[,"b4gb"])
  new.d <- data.frame(new.d, b4gb)
  new.d <- apply_labels(new.d, b4gb = "Asthma, COPD age")
  temp.d <- data.frame (new.d, b4gb)  
  result<-questionr::freq(temp.d$b4gb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Asthma, COPD Age")
Asthma, COPD Age
n % val%
14 1 0.4 2.4
19 1 0.4 2.4
25 1 0.4 2.4
3 1 0.4 2.4
30 2 0.7 4.8
45 1 0.4 2.4
5 6 2.1 14.3
50 4 1.4 9.5
52 1 0.4 2.4
55 2 0.7 4.8
56 1 0.4 2.4
58 1 0.4 2.4
6 2 0.7 4.8
60 2 0.7 4.8
62 1 0.4 2.4
63 1 0.4 2.4
64 2 0.7 4.8
65 1 0.4 2.4
67 1 0.4 2.4
69 1 0.4 2.4
7 5 1.8 11.9
70 1 0.4 2.4
77 1 0.4 2.4
9 1 0.4 2.4
97 1 0.4 2.4
NA 240 85.1 NA
Total 282 100.0 100.0 
# Stomach ulcers
  b4ha <- as.factor(d[,"b4ha"])
  # Make "*" to NA
b4ha[which(b4ha=="*")]<-"NA"
  levels(b4ha) <- list(No="1",
                     Yes="2")
  b4ha <- ordered(b4ha, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ha)
  new.d <- apply_labels(new.d, b4ha = "Stomach ulcers")
  temp.d <- data.frame (new.d, b4ha)  
  
  result<-questionr::freq(temp.d$b4ha, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stomach ulcers")
Stomach ulcers
n % val%
No 250 88.7 92.9
Yes 19 6.7 7.1
NA 13 4.6 NA
Total 282 100.0 100.0 
  b4hb <- as.factor(d[,"b4hb"])
  new.d <- data.frame(new.d, b4hb)
  new.d <- apply_labels(new.d, b4hb = "Stomach ulcers age")
  temp.d <- data.frame (new.d, b4hb)  
  result<-questionr::freq(temp.d$b4hb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stomach ulcers Age")
Stomach ulcers Age
n % val%
15 1 0.4 5.9
18 1 0.4 5.9
19 1 0.4 5.9
21 1 0.4 5.9
24 1 0.4 5.9
25 1 0.4 5.9
35 3 1.1 17.6
36 1 0.4 5.9
45 2 0.7 11.8
50 1 0.4 5.9
65 2 0.7 11.8
66 1 0.4 5.9
72 1 0.4 5.9
NA 265 94.0 NA
Total 282 100.0 100.0 
# Crohn's Disease
  b4ia <- as.factor(d[,"b4ia"])
  # Make "*" to NA
b4ia[which(b4ia=="*")]<-"NA"
  levels(b4ia) <- list(No="1",
                     Yes="2")
  b4ia <- ordered(b4ia, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ia)
  new.d <- apply_labels(new.d, b4ia = "Crohn's Disease")
  temp.d <- data.frame (new.d, b4ia)  
  
  result<-questionr::freq(temp.d$b4ia, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Crohn's Disease")
Crohn’s Disease
n % val%
No 262 92.9 97.8
Yes 6 2.1 2.2
NA 14 5.0 NA
Total 282 100.0 100.0 
  b4ib <- as.factor(d[,"b4ib"])
  new.d <- data.frame(new.d, b4ib)
  new.d <- apply_labels(new.d, b4ib = "Crohn's Disease age")
  temp.d <- data.frame (new.d, b4ib)  
  result<-questionr::freq(temp.d$b4ib, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Crohn's Disease Age")
Crohn’s Disease Age
n % val%
15 1 0.4 20
35 1 0.4 20
58 1 0.4 20
71 1 0.4 20
75 1 0.4 20
NA 277 98.2 NA
Total 282 100.0 100 
# Diabetes
  b4ja <- as.factor(d[,"b4ja"])
  # Make "*" to NA
b4ja[which(b4ja=="*")]<-"NA"
  levels(b4ja) <- list(No="1",
                     Yes="2")
  b4ja <- ordered(b4ja, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ja)
  new.d <- apply_labels(new.d, b4ja = "Diabetes")
  temp.d <- data.frame (new.d, b4ja)  
  
  result<-questionr::freq(temp.d$b4ja, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Diabetes")
Diabetes
n % val%
No 188 66.7 68.9
Yes 85 30.1 31.1
NA 9 3.2 NA
Total 282 100.0 100.0 
  b4jb <- as.factor(d[,"b4jb"])
  new.d <- data.frame(new.d, b4jb)
  new.d <- apply_labels(new.d, b4jb = "Diabetes age")
  temp.d <- data.frame (new.d, b4jb)  
  result<-questionr::freq(temp.d$b4jb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Diabetes Age")
Diabetes Age
n % val%
29 1 0.4 1.3
3 1 0.4 1.3
35 1 0.4 1.3
37 1 0.4 1.3
38 1 0.4 1.3
39 2 0.7 2.6
40 5 1.8 6.5
41 1 0.4 1.3
42 1 0.4 1.3
45 2 0.7 2.6
47 2 0.7 2.6
48 2 0.7 2.6
50 5 1.8 6.5
51 1 0.4 1.3
52 2 0.7 2.6
53 1 0.4 1.3
54 2 0.7 2.6
55 8 2.8 10.4
56 3 1.1 3.9
57 2 0.7 2.6
58 3 1.1 3.9
59 3 1.1 3.9
60 7 2.5 9.1
61 1 0.4 1.3
62 2 0.7 2.6
63 1 0.4 1.3
64 2 0.7 2.6
65 7 2.5 9.1
68 2 0.7 2.6
70 3 1.1 3.9
76 1 0.4 1.3
95 1 0.4 1.3
NA 205 72.7 NA
Total 282 100.0 100.0 
# Kidney Problems
  b4ka <- as.factor(d[,"b4ka"])
  # Make "*" to NA
b4ka[which(b4ka=="*")]<-"NA"
  levels(b4ka) <- list(No="1",
                     Yes="2")
  b4ka <- ordered(b4ka, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ka)
  new.d <- apply_labels(new.d, b4ka = "Kidney Problems")
  temp.d <- data.frame (new.d, b4ka)  
  
  result<-questionr::freq(temp.d$b4ka, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Kidney Problems")
Kidney Problems
n % val%
No 255 90.4 95.1
Yes 13 4.6 4.9
NA 14 5.0 NA
Total 282 100.0 100.0 
  b4kb <- as.factor(d[,"b4kb"])
  new.d <- data.frame(new.d, b4kb)
  new.d <- apply_labels(new.d, b4kb = "Kidney Problems age")
  temp.d <- data.frame (new.d, b4kb)  
  result<-questionr::freq(temp.d$b4kb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Kidney Problems Age")
Kidney Problems Age
n % val%
40 1 0.4 8.3
50 3 1.1 25.0
57 1 0.4 8.3
59 1 0.4 8.3
62 1 0.4 8.3
68 1 0.4 8.3
71 2 0.7 16.7
74 1 0.4 8.3
77 1 0.4 8.3
NA 270 95.7 NA
Total 282 100.0 100.0 
# Cirrhosis, liver damage
  b4la <- as.factor(d[,"b4la"])
  # Make "*" to NA
b4la[which(b4la=="*")]<-"NA"
  levels(b4la) <- list(No="1",
                     Yes="2")
  b4la <- ordered(b4la, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4la)
  new.d <- apply_labels(new.d, b4la = "Cirrhosis, liver damage")
  temp.d <- data.frame (new.d, b4la)  
  
  result<-questionr::freq(temp.d$b4la, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage")
Cirrhosis, liver damage
n % val%
No 264 93.6 97.1
Yes 8 2.8 2.9
NA 10 3.5 NA
Total 282 100.0 100.0 
  b4lb <- as.factor(d[,"b4lb"])
  new.d <- data.frame(new.d, b4lb)
  new.d <- apply_labels(new.d, b4lb = "Cirrhosis, liver damage age")
  temp.d <- data.frame (new.d, b4lb)  
  result<-questionr::freq(temp.d$b4lb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage Age")
Cirrhosis, liver damage Age
n % val%
50 1 0.4 25
60 2 0.7 50
67 1 0.4 25
NA 278 98.6 NA
Total 282 100.0 100 
# Arthritis
  b4ma <- as.factor(d[,"b4ma"])
  # Make "*" to NA
b4ma[which(b4ma=="*")]<-"NA"
  levels(b4ma) <- list(No="1",
                     Yes="2")
  b4ma <- ordered(b4ma, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ma)
  new.d <- apply_labels(new.d, b4ma = "Arthritis")
  temp.d <- data.frame (new.d, b4ma)  
  
  result<-questionr::freq(temp.d$b4ma, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Arthritis")
Arthritis
n % val%
No 248 87.9 92.9
Yes 19 6.7 7.1
NA 15 5.3 NA
Total 282 100.0 100.0 
  b4mb <- as.factor(d[,"b4mb"])
  new.d <- data.frame(new.d, b4mb)
  new.d <- apply_labels(new.d, b4mb = "Arthritis age")
  temp.d <- data.frame (new.d, b4mb)  
  result<-questionr::freq(temp.d$b4mb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Arthritis Age")
Arthritis Age
n % val%
30 1 0.4 5.3
32 1 0.4 5.3
40 2 0.7 10.5
42 1 0.4 5.3
45 4 1.4 21.1
48 1 0.4 5.3
51 1 0.4 5.3
55 1 0.4 5.3
56 1 0.4 5.3
57 1 0.4 5.3
60 3 1.1 15.8
62 1 0.4 5.3
72 1 0.4 5.3
NA 263 93.3 NA
Total 282 100.0 100.0 
# Dementia
  b4na <- as.factor(d[,"b4na"])
  # Make "*" to NA
b4na[which(b4na=="*")]<-"NA"
  levels(b4na) <- list(No="1",
                     Yes="2")
  b4na <- ordered(b4na, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4na)
  new.d <- apply_labels(new.d, b4na = "Dementia")
  temp.d <- data.frame (new.d, b4na)  
  
  result<-questionr::freq(temp.d$b4na, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Dementia")
Dementia
n % val%
No 268 95.0 98.9
Yes 3 1.1 1.1
NA 11 3.9 NA
Total 282 100.0 100.0 
  b4nb <- as.factor(d[,"b4nb"])
  new.d <- data.frame(new.d, b4nb)
  new.d <- apply_labels(new.d, b4nb = "Dementia age")
  temp.d <- data.frame (new.d, b4nb)  
  result<-questionr::freq(temp.d$b4nb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Dementia Age")
Dementia Age
n % val%
29 1 0.4 25
57 1 0.4 25
60 1 0.4 25
72 1 0.4 25
NA 278 98.6 NA
Total 282 100.0 100 
# Depression 
  b4oa <- as.factor(d[,"b4oa"])
  # Make "*" to NA
b4oa[which(b4oa=="*")]<-"NA"
  levels(b4oa) <- list(No="1",
                     Yes="2")
  b4oa <- ordered(b4oa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4oa)
  new.d <- apply_labels(new.d, b4oa = "Depression")
  temp.d <- data.frame (new.d, b4oa)  
  
  result<-questionr::freq(temp.d$b4oa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Depression")
Depression
n % val%
No 242 85.8 89.6
Yes 28 9.9 10.4
NA 12 4.3 NA
Total 282 100.0 100.0 
  b4ob <- as.factor(d[,"b4ob"])
  new.d <- data.frame(new.d, b4ob)
  new.d <- apply_labels(new.d, b4ob = "Depression age")
  temp.d <- data.frame (new.d, b4ob)  
  result<-questionr::freq(temp.d$b4ob, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Depression Age")
Depression Age
n % val%
14 1 0.4 4.2
19 2 0.7 8.3
30 1 0.4 4.2
38 1 0.4 4.2
39 1 0.4 4.2
40 2 0.7 8.3
42 1 0.4 4.2
44 1 0.4 4.2
49 1 0.4 4.2
52 1 0.4 4.2
53 1 0.4 4.2
57 1 0.4 4.2
60 1 0.4 4.2
61 1 0.4 4.2
65 1 0.4 4.2
66 2 0.7 8.3
68 1 0.4 4.2
70 3 1.1 12.5
74 1 0.4 4.2
NA 258 91.5 NA
Total 282 100.0 100.0 
# AIDS
  b4pa <- as.factor(d[,"b4pa"])
  # Make "*" to NA
b4pa[which(b4pa=="*")]<-"NA"
  levels(b4pa) <- list(No="1",
                     Yes="2")
  b4pa <- ordered(b4pa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4pa)
  new.d <- apply_labels(new.d, b4pa = "AIDS")
  temp.d <- data.frame (new.d, b4pa)  
  
  result<-questionr::freq(temp.d$b4pa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "AIDS")
AIDS
n % val%
No 269 95.4 98.5
Yes 4 1.4 1.5
NA 9 3.2 NA
Total 282 100.0 100.0 
  b4pb <- as.factor(d[,"b4pb"])
  new.d <- data.frame(new.d, b4pb)
  new.d <- apply_labels(new.d, b4pb = "AIDS age")
  temp.d <- data.frame (new.d, b4pb)  
  result<-questionr::freq(temp.d$b4pb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "AIDS Age")
AIDS Age
n % val%
30 1 0.4 33.3
63 1 0.4 33.3
9 1 0.4 33.3
NA 279 98.9 NA
Total 282 100.0 100.0 
# Other Cancer
  b4qa <- as.factor(d[,"b4qa"])
  # Make "*" to NA
b4qa[which(b4qa=="*")]<-"NA"
  levels(b4qa) <- list(No="1",
                     Yes="2")
  b4qa <- ordered(b4qa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4qa)
  new.d <- apply_labels(new.d, b4qa = "Other Cancer")
  temp.d <- data.frame (new.d, b4qa)  
  
  result<-questionr::freq(temp.d$b4qa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Other Cancer")
Other Cancer
n % val%
No 246 87.2 93.5
Yes 17 6.0 6.5
NA 19 6.7 NA
Total 282 100.0 100.0 
  b4qb <- as.factor(d[,"b4qb"])
  new.d <- data.frame(new.d, b4qb)
  new.d <- apply_labels(new.d, b4qb = "Other Cancer age")
  temp.d <- data.frame (new.d, b4qb)  
  result<-questionr::freq(temp.d$b4qb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Other Cancer Age")
Other Cancer Age
n % val%
36 1 0.4 6.7
40 1 0.4 6.7
50 4 1.4 26.7
54 2 0.7 13.3
61 2 0.7 13.3
63 2 0.7 13.3
65 1 0.4 6.7
67 1 0.4 6.7
69 1 0.4 6.7
NA 267 94.7 NA
Total 282 100.0 100.0

B4Q Other Cancer

b4qother <- d[,"b4qother"]
  new.d <- data.frame(new.d, b4qother)
  new.d <- apply_labels(new.d, b4qother = "b4qother")
  temp.d <- data.frame (new.d, b4qother)
result<-questionr::freq(temp.d$b4qother, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B4Q Other")
B4Q Other
n % val%
Colon 3 1.1 20.0
Colon (polyps) 1 0.4 6.7
Colon cancer 1 0.4 6.7
Glaucoma cancer 1 0.4 6.7
Kaposis Sarcoma 1 0.4 6.7
Kidney-colon. 1 0.4 6.7
Liver 1 0.4 6.7
Lung cancer 1 0.4 6.7
Lymphoma 2 0.7 13.3
Melanoma 1 0.4 6.7
Skin cancer 1 0.4 6.7
Testicular 1 0.4 6.7
NA 267 94.7 NA
Total 282 100.0 100.0

B5: Routine care

  • B5. Where do you usually go for routine medical care (seeing a doctor for any reason, not just for cancer care)?
    • 1=Community health center or free clinic
    • 2=Hospital (not emergency)/ urgent care clinic
    • 3=Private doctor’s office
    • 4=Emergency room
    • 5=Veteran’s Affairs/VA
    • 6=Other type of location
  b5 <- as.factor(d[,"b5"])
# Make "*" to NA
b5[which(b5=="*")]<-"NA"
  levels(b5) <- list(Community_center_free_clinic="1",
                     Hospital_urgent_care_clinic="2",
                     Private_Dr_office="3",
                     ER="4",
                     VA="5",
                     Other="6")
  b5 <- ordered(b5, c("Community_center_free_clinic", "Hospital_urgent_care_clinic", "Private_Dr_office", "ER","VA","Other"))
  
  new.d <- data.frame(new.d, b5)
  new.d <- apply_labels(new.d, b5 = "routine medical care")
  temp.d <- data.frame (new.d, b5)  
  
  result<-questionr::freq(temp.d$b5 ,total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val%
Community_center_free_clinic 22 7.8 8.5
Hospital_urgent_care_clinic 24 8.5 9.2
Private_Dr_office 189 67.0 72.7
ER 2 0.7 0.8
VA 17 6.0 6.5
Other 6 2.1 2.3
NA 22 7.8 NA
Total 282 100.0 100.0

B5 Other: Routine care

b5other <- d[,"b5other"]
  new.d <- data.frame(new.d, b5other)
  new.d <- apply_labels(new.d, b5other = "b5other")
  temp.d <- data.frame (new.d, b5other)
result<-questionr::freq(temp.d$b5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B5 Other")
B5 Other
n % val%
29 Palms Navel Hospital 1 0.4 7.1
Alpha Medical 1 0.4 7.1
Dialysis center 1 0.4 7.1
Doctors office (primary Dr) 1 0.4 7.1
Facey Med Group 1 0.4 7.1
In my doctors office 1 0.4 7.1
Kaiser 3 1.1 21.4
Lab quest PSA levels every three months. 1 0.4 7.1
Medical center 1 0.4 7.1
Private clinics 1 0.4 7.1
UCD Medical office 1 0.4 7.1
Urologist specialist 1 0.4 7.1
NA 268 95.0 NA
Total 282 100.0 100.0

C1: Years lived at current address

  • C1. How many years have you lived in your current address?
    • 1=Less than 1 year
    • 2=1-5 years
    • 3=6-10 years
    • 4=11-15 years
    • 5=16-20 years
    • 6=21+ years
  c1 <- as.factor(d[,"c1"])
# Make "*" to NA
c1[which(c1=="*")]<-"NA"
  levels(c1) <- list(Less_than_1_year="1",
                     years_1_5="2",
                     years_6_10="3",
                     years_11_15="4",
                     years_16_20="5",
                     years_21_more="6")
  c1 <- ordered(c1, c("Less_than_1_year", "years_1_5", "years_6_10", "years_11_15","years_16_20","years_21_more"))
  
  new.d <- data.frame(new.d, c1)
  new.d <- apply_labels(new.d, c1 = "living period")
  temp.d <- data.frame (new.d, c1)  
  
  result<-questionr::freq(temp.d$c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val% %cum val%cum
Less_than_1_year 10 3.5 3.7 3.5 3.7
years_1_5 64 22.7 23.4 26.2 27.1
years_6_10 46 16.3 16.8 42.6 44.0
years_11_15 40 14.2 14.7 56.7 58.6
years_16_20 42 14.9 15.4 71.6 74.0
years_21_more 71 25.2 26.0 96.8 100.0
NA 9 3.2 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

C2A: Feel safe walking in the neighborhood

    1. On average, I felt/feel safe walking in my neighborhood day or night.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2a1 <- as.factor(d[,"c2a1"])
# Make "*" to NA
c2a1[which(c2a1=="*")]<-"NA"
  levels(c2a1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a1 <- ordered(c2a1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a1)
  new.d <- apply_labels(new.d, c2a1 = "walk in the neighborhood-current")
  temp.d <- data.frame (new.d, c2a1)  
  
  c2a2 <- as.factor(d[,"c2a2"])
  # Make "*" to NA
c2a2[which(c2a2=="*")]<-"NA"
  levels(c2a2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a2 <- ordered(c2a2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a2)
  new.d <- apply_labels(new.d, c2a2 = "walk in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2a2) 
  
  c2a3 <- as.factor(d[,"c2a3"])
  # Make "*" to NA
c2a3[which(c2a3=="*")]<-"NA"
  levels(c2a3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a3 <- ordered(c2a3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a3)
  new.d <- apply_labels(new.d, c2a3 = "walk in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2a3)
  
  result<-questionr::freq(temp.d$c2a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 136 48.2 49.8 48.2 49.8
Agree 94 33.3 34.4 81.6 84.2
Neutral 24 8.5 8.8 90.1 93.0
Disagree 14 5.0 5.1 95.0 98.2
Strongly_Disagree 5 1.8 1.8 96.8 100.0
NA 9 3.2 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 108 38.3 41.1 38.3 41.1
Agree 93 33.0 35.4 71.3 76.4
Neutral 41 14.5 15.6 85.8 92.0
Disagree 15 5.3 5.7 91.1 97.7
Strongly_Disagree 6 2.1 2.3 93.3 100.0
NA 19 6.7 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 101 35.8 38.5 35.8 38.5
Agree 77 27.3 29.4 63.1 67.9
Neutral 53 18.8 20.2 81.9 88.2
Disagree 25 8.9 9.5 90.8 97.7
Strongly_Disagree 6 2.1 2.3 92.9 100.0
NA 20 7.1 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

C2B: Violence

    1. Violence was/is not a problem in my neighborhood.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2b1 <- as.factor(d[,"c2b1"])
# Make "*" to NA
c2b1[which(c2b1=="*")]<-"NA"
  levels(c2b1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b1 <- ordered(c2b1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b1)
  new.d <- apply_labels(new.d, c2b1 = "Violence in the neighborhood-current")
  temp.d <- data.frame (new.d, c2b1)  
  
  c2b2 <- as.factor(d[,"c2b2"])
  # Make "*" to NA
c2b2[which(c2b2=="*")]<-"NA"
  levels(c2b2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b2 <- ordered(c2b2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b2)
  new.d <- apply_labels(new.d, c2b2 = "Violence in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2b2) 
  
  c2b3 <- as.factor(d[,"c2b3"])
  # Make "*" to NA
c2b3[which(c2b3=="*")]<-"NA"
  levels(c2b3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b3 <- ordered(c2b3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b3)
  new.d <- apply_labels(new.d, c2b3 = "Violence in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2b3)
  
  result<-questionr::freq(temp.d$c2b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 121 42.9 44.3 42.9 44.3
Agree 83 29.4 30.4 72.3 74.7
Neutral 38 13.5 13.9 85.8 88.6
Disagree 22 7.8 8.1 93.6 96.7
Strongly_Disagree 9 3.2 3.3 96.8 100.0
NA 9 3.2 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 96 34.0 36.8 34.0 36.8
Agree 83 29.4 31.8 63.5 68.6
Neutral 49 17.4 18.8 80.9 87.4
Disagree 22 7.8 8.4 88.7 95.8
Strongly_Disagree 11 3.9 4.2 92.6 100.0
NA 21 7.4 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 73 25.9 27.9 25.9 27.9
Agree 72 25.5 27.5 51.4 55.3
Neutral 61 21.6 23.3 73.0 78.6
Disagree 41 14.5 15.6 87.6 94.3
Strongly_Disagree 15 5.3 5.7 92.9 100.0
NA 20 7.1 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

C2C: Safe from crime

    1. My neighborhood was/is safe from crime.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2c1 <- as.factor(d[,"c2c1"])
# Make "*" to NA
c2c1[which(c2c1=="*")]<-"NA"
  levels(c2c1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c1 <- ordered(c2c1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c1)
  new.d <- apply_labels(new.d, c2c1 = "safe from crime in the neighborhood-current")
  temp.d <- data.frame (new.d, c2c1)  
  
  c2c2 <- as.factor(d[,"c2c2"])
  # Make "*" to NA
c2c2[which(c2c2=="*")]<-"NA"
  levels(c2c2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c2 <- ordered(c2c2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c2)
  new.d <- apply_labels(new.d, c2c2 = "safe from crime in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2c2) 
  
  c2c3 <- as.factor(d[,"c2c3"])
  # Make "*" to NA
c2c3[which(c2c3=="*")]<-"NA"
  levels(c2c3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c3 <- ordered(c2c3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c3)
  new.d <- apply_labels(new.d, c2c3 = "safe from crime in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2c3)
  
  result<-questionr::freq(temp.d$c2c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 73 25.9 27.1 25.9 27.1
Agree 90 31.9 33.5 57.8 60.6
Neutral 71 25.2 26.4 83.0 87.0
Disagree 28 9.9 10.4 92.9 97.4
Strongly_Disagree 7 2.5 2.6 95.4 100.0
NA 13 4.6 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 54 19.1 20.9 19.1 20.9
Agree 97 34.4 37.6 53.5 58.5
Neutral 70 24.8 27.1 78.4 85.7
Disagree 28 9.9 10.9 88.3 96.5
Strongly_Disagree 9 3.2 3.5 91.5 100.0
NA 24 8.5 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 55 19.5 21.2 19.5 21.2
Agree 68 24.1 26.3 43.6 47.5
Neutral 72 25.5 27.8 69.1 75.3
Disagree 43 15.2 16.6 84.4 91.9
Strongly_Disagree 21 7.4 8.1 91.8 100.0
NA 23 8.2 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

C3A: Traffic

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Traffic
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3a1 <- as.factor(d[,"c3a1"])
# Make "*" to NA
c3a1[which(c3a1=="*")]<-"NA"
  levels(c3a1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a1 <- ordered(c3a1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a1)
  new.d <- apply_labels(new.d, c3a1 = "A lot of noise-Current")
  temp.d <- data.frame (new.d, c3a1)  
  
  c3a2 <- as.factor(d[,"c3a2"])
  # Make "*" to NA
c3a2[which(c3a2=="*")]<-"NA"
  levels(c3a2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a2 <- ordered(c3a2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a2)
  new.d <- apply_labels(new.d, c3a2 = "A lot of noise-age 31 up")
  temp.d <- data.frame (new.d, c3a2) 
  
  c3a3 <- as.factor(d[,"c3a3"])
  # Make "*" to NA
c3a3[which(c3a3=="*")]<-"NA"
  levels(c3a3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a3 <- ordered(c3a3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a3)
  new.d <- apply_labels(new.d, c3a3 = "A lot of noise-Childhood or young")
  temp.d <- data.frame (new.d, c3a3)
  
  result<-questionr::freq(temp.d$c3a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 193 68.4 71.0 68.4 71.0
Somewhat_serious 54 19.1 19.9 87.6 90.8
Very_serious 17 6.0 6.2 93.6 97.1
Dont_know 8 2.8 2.9 96.5 100.0
NA 10 3.5 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 173 61.3 65.0 61.3 65.0
Somewhat_serious 56 19.9 21.1 81.2 86.1
Very_serious 21 7.4 7.9 88.7 94.0
Dont_know 16 5.7 6.0 94.3 100.0
NA 16 5.7 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 186 66.0 70.2 66.0 70.2
Somewhat_serious 48 17.0 18.1 83.0 88.3
Very_serious 12 4.3 4.5 87.2 92.8
Dont_know 19 6.7 7.2 94.0 100.0
NA 17 6.0 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

C3B: Noise

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. A lot of noise
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3b1 <- as.factor(d[,"c3b1"])
# Make "*" to NA
c3b1[which(c3b1=="*")]<-"NA"
  levels(c3b1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b1 <- ordered(c3b1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b1)
  new.d <- apply_labels(new.d, c3b1 = "A lot of noise-Current")
  temp.d <- data.frame (new.d, c3b1)  
  
  c3b2 <- as.factor(d[,"c3b2"])
  # Make "*" to NA
c3b2[which(c3b2=="*")]<-"NA"
  levels(c3b2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b2 <- ordered(c3b2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b2)
  new.d <- apply_labels(new.d, c3b2 = "A lot of noise-age 31 up")
  temp.d <- data.frame (new.d, c3b2) 
  
  c3b3 <- as.factor(d[,"c3b3"])
  # Make "*" to NA
c3b3[which(c3b3=="*")]<-"NA"
  levels(c3b3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b3 <- ordered(c3b3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b3)
  new.d <- apply_labels(new.d, c3b3 = "A lot of noise-Childhood or young")
  temp.d <- data.frame (new.d, c3b3)
  
  result<-questionr::freq(temp.d$c3b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 229 81.2 84.2 81.2 84.2
Somewhat_serious 30 10.6 11.0 91.8 95.2
Very_serious 7 2.5 2.6 94.3 97.8
Dont_know 6 2.1 2.2 96.5 100.0
NA 10 3.5 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 205 72.7 77.4 72.7 77.4
Somewhat_serious 41 14.5 15.5 87.2 92.8
Very_serious 7 2.5 2.6 89.7 95.5
Dont_know 12 4.3 4.5 94.0 100.0
NA 17 6.0 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 184 65.2 69.4 65.2 69.4
Somewhat_serious 52 18.4 19.6 83.7 89.1
Very_serious 8 2.8 3.0 86.5 92.1
Dont_know 21 7.4 7.9 94.0 100.0
NA 17 6.0 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

C3C: Trash and litter

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Trash and litter
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3c1 <- as.factor(d[,"c3c1"])
# Make "*" to NA
c3c1[which(c3c1=="*")]<-"NA"
  levels(c3c1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c1 <- ordered(c3c1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c1)
  new.d <- apply_labels(new.d, c3c1 = "Trash and litter-Current")
  temp.d <- data.frame (new.d, c3c1)  
  
  c3c2 <- as.factor(d[,"c3c2"])
  # Make "*" to NA
c3c2[which(c3c2=="*")]<-"NA"
  levels(c3c2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c2 <- ordered(c3c2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c2)
  new.d <- apply_labels(new.d, c3c2 = "Trash and litter-age 31 up")
  temp.d <- data.frame (new.d, c3c2) 
  
  c3c3 <- as.factor(d[,"c3c3"])
  # Make "*" to NA
c3c3[which(c3c3=="*")]<-"NA"
  levels(c3c3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c3 <- ordered(c3c3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c3)
  new.d <- apply_labels(new.d, c3c3 = "Trash and litter-Childhood or young")
  temp.d <- data.frame (new.d, c3c3)
  
  result<-questionr::freq(temp.d$c3c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 232 82.3 85.6 82.3 85.6
Somewhat_serious 22 7.8 8.1 90.1 93.7
Very_serious 14 5.0 5.2 95.0 98.9
Dont_know 3 1.1 1.1 96.1 100.0
NA 11 3.9 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 207 73.4 78.4 73.4 78.4
Somewhat_serious 37 13.1 14.0 86.5 92.4
Very_serious 11 3.9 4.2 90.4 96.6
Dont_know 9 3.2 3.4 93.6 100.0
NA 18 6.4 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 187 66.3 70.8 66.3 70.8
Somewhat_serious 40 14.2 15.2 80.5 86.0
Very_serious 19 6.7 7.2 87.2 93.2
Dont_know 18 6.4 6.8 93.6 100.0
NA 18 6.4 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

C3D: Too much light at night

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Too much light at night
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3d1 <- as.factor(d[,"c3d1"])
# Make "*" to NA
c3d1[which(c3d1=="*")]<-"NA"
  levels(c3d1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d1 <- ordered(c3d1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d1)
  new.d <- apply_labels(new.d, c3d1 = "Too much light at night-Current")
  temp.d <- data.frame (new.d, c3d1)  
  
  c3d2 <- as.factor(d[,"c3d2"])
  # Make "*" to NA
c3d2[which(c3d2=="*")]<-"NA"
  levels(c3d2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d2 <- ordered(c3d2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d2)
  new.d <- apply_labels(new.d, c3d2 = "Too much light at night-age 31 up")
  temp.d <- data.frame (new.d, c3d2) 
  
  c3d3 <- as.factor(d[,"c3d3"])
  # Make "*" to NA
c3d3[which(c3d3=="*")]<-"NA"
  levels(c3d3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d3 <- ordered(c3d3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d3)
  new.d <- apply_labels(new.d, c3d3 = "Too much light at night-Childhood or young")
  temp.d <- data.frame (new.d, c3d3)
  
  result<-questionr::freq(temp.d$c3d1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 246 87.2 91.1 87.2 91.1
Somewhat_serious 8 2.8 3.0 90.1 94.1
Very_serious 5 1.8 1.9 91.8 95.9
Dont_know 11 3.9 4.1 95.7 100.0
NA 12 4.3 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3d2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 233 82.6 88.6 82.6 88.6
Somewhat_serious 15 5.3 5.7 87.9 94.3
Very_serious 3 1.1 1.1 89.0 95.4
Dont_know 12 4.3 4.6 93.3 100.0
NA 19 6.7 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3d3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 223 79.1 84.8 79.1 84.8
Somewhat_serious 14 5.0 5.3 84.0 90.1
Very_serious 4 1.4 1.5 85.5 91.6
Dont_know 22 7.8 8.4 93.3 100.0
NA 19 6.7 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

C4A: Neighbors talking outside

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How often do/did you see neighbors talking outside in the yard, on the street, at the corner park, etc.?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4a1 <- as.factor(d[,"c4a1"])
# Make "*" to NA
c4a1[which(c4a1=="*")]<-"NA"
  levels(c4a1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a1 <- ordered(c4a1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a1)
  new.d <- apply_labels(new.d, c4a1 = "Talk outside-Current")
  temp.d <- data.frame (new.d, c4a1)  
  
  c4a2 <- as.factor(d[,"c4a2"])
# Make "*" to NA
c4a2[which(c4a2=="*")]<-"NA" 
  levels(c4a2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a2 <- ordered(c4a2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a2)
  new.d <- apply_labels(new.d, c4a2 = "Talk outside-age 31 up")
  temp.d <- data.frame (new.d, c4a2) 
  
  c4a3 <- as.factor(d[,"c4a3"])
  # Make "*" to NA
c4a3[which(c4a3=="*")]<-"NA"
  levels(c4a3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a3 <- ordered(c4a3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a3)
  new.d <- apply_labels(new.d, c4a3 = "Talk outside-Childhood or young")
  temp.d <- data.frame (new.d, c4a3)
  
  result<-questionr::freq(temp.d$c4a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 108 38.3 40.0 38.3 40.0
Sometimes 107 37.9 39.6 76.2 79.6
Rarely_Never 52 18.4 19.3 94.7 98.9
Dont_know 3 1.1 1.1 95.7 100.0
NA 12 4.3 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 98 34.8 37.0 34.8 37.0
Sometimes 119 42.2 44.9 77.0 81.9
Rarely_Never 36 12.8 13.6 89.7 95.5
Dont_know 12 4.3 4.5 94.0 100.0
NA 17 6.0 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 149 52.8 57.1 52.8 57.1
Sometimes 66 23.4 25.3 76.2 82.4
Rarely_Never 26 9.2 10.0 85.5 92.3
Dont_know 20 7.1 7.7 92.6 100.0
NA 21 7.4 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

C4B: Neighbors watch out for each other

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How often do/did neighbors watch out for each other, such as calling if they see a problem?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4b1 <- as.factor(d[,"c4b1"])
# Make "*" to NA
c4b1[which(c4b1=="*")]<-"NA"
  levels(c4b1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b1 <- ordered(c4b1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b1)
  new.d <- apply_labels(new.d, c4b1 = "watch out-Current")
  temp.d <- data.frame (new.d, c4b1)  
  
  c4b2 <- as.factor(d[,"c4b2"])
  # Make "*" to NA
c4b2[which(c4b2=="*")]<-"NA"
  levels(c4b2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b2 <- ordered(c4b2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b2)
  new.d <- apply_labels(new.d, c4b2 = "watch out-age 31 up")
  temp.d <- data.frame (new.d, c4b2) 
  
  c4b3 <- as.factor(d[,"c4b3"])
  # Make "*" to NA
c4b3[which(c4b3=="*")]<-"NA"
  levels(c4b3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b3 <- ordered(c4b3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b3)
  new.d <- apply_labels(new.d, c4b3 = "watch out-Childhood or young")
  temp.d <- data.frame (new.d, c4b3)
  
  result<-questionr::freq(temp.d$c4b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 115 40.8 43.2 40.8 43.2
Sometimes 86 30.5 32.3 71.3 75.6
Rarely_Never 42 14.9 15.8 86.2 91.4
Dont_know 23 8.2 8.6 94.3 100.0
NA 16 5.7 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 85 30.1 32.9 30.1 32.9
Sometimes 95 33.7 36.8 63.8 69.8
Rarely_Never 48 17.0 18.6 80.9 88.4
Dont_know 30 10.6 11.6 91.5 100.0
NA 24 8.5 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 114 40.4 44.7 40.4 44.7
Sometimes 68 24.1 26.7 64.5 71.4
Rarely_Never 34 12.1 13.3 76.6 84.7
Dont_know 39 13.8 15.3 90.4 100.0
NA 27 9.6 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

C4C: Neighbors know by name

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors do/did you know by name?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4c1 <- as.factor(d[,"c4c1"])
# Make "*" to NA
c4c1[which(c4c1=="*")]<-"NA"
  levels(c4c1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c1 <- ordered(c4c1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c1)
  new.d <- apply_labels(new.d, c4c1 = "Know names-Current")
  temp.d <- data.frame (new.d, c4c1)  
  
  c4c2 <- as.factor(d[,"c4c2"])
# Make "*" to NA
c4c2[which(c4c2=="*")]<-"NA"
  levels(c4c2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c2 <- ordered(c4c2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c2)
  new.d <- apply_labels(new.d, c4c2 = "Know names-age 31 up")
  temp.d <- data.frame (new.d, c4c2) 
  
  c4c3 <- as.factor(d[,"c4c3"])
# Make "*" to NA
c4c3[which(c4c3=="*")]<-"NA"
  levels(c4c3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c3 <- ordered(c4c3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c3)
  new.d <- apply_labels(new.d, c4c3 = "Know names-Childhood or young")
  temp.d <- data.frame (new.d, c4c3)
  
  result<-questionr::freq(temp.d$c4c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 71 25.2 27.2 25.2 27.2
Sometimes 103 36.5 39.5 61.7 66.7
Rarely_Never 86 30.5 33.0 92.2 99.6
Dont_know 1 0.4 0.4 92.6 100.0
NA 21 7.4 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 64 22.7 25.5 22.7 25.5
Sometimes 101 35.8 40.2 58.5 65.7
Rarely_Never 75 26.6 29.9 85.1 95.6
Dont_know 11 3.9 4.4 89.0 100.0
NA 31 11.0 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 141 50.0 56.0 50.0 56.0
Sometimes 59 20.9 23.4 70.9 79.4
Rarely_Never 36 12.8 14.3 83.7 93.7
Dont_know 16 5.7 6.3 89.4 100.0
NA 30 10.6 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

C4D: Friendly talks with neighbors

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors do/did you have a friendly talk with at least once a week?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4d1 <- as.factor(d[,"c4d1"])
# Make "*" to NA
c4d1[which(c4d1=="*")]<-"NA"
  levels(c4d1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d1 <- ordered(c4d1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d1)
  new.d <- apply_labels(new.d, c4d1 = "Know names-Current")
  temp.d <- data.frame (new.d, c4d1)  
  
  c4d2 <- as.factor(d[,"c4d2"])
# Make "*" to NA
c4d2[which(c4d2=="*")]<-"NA"
  levels(c4d2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d2 <- ordered(c4d2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d2)
  new.d <- apply_labels(new.d, c4d2 = "Know names-age 31 up")
  temp.d <- data.frame (new.d, c4d2) 
  
  c4d3 <- as.factor(d[,"c4d3"])
  # Make "*" to NA
c4d3[which(c4d3=="*")]<-"NA"
  levels(c4d3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d3 <- ordered(c4d3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d3)
  new.d <- apply_labels(new.d, c4d3 = "Know names-Childhood or young")
  temp.d <- data.frame (new.d, c4d3)
  
  result<-questionr::freq(temp.d$c4d1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 41 14.5 15.2 14.5 15.2
Sometimes 97 34.4 35.9 48.9 51.1
Rarely_Never 129 45.7 47.8 94.7 98.9
Dont_know 3 1.1 1.1 95.7 100.0
NA 12 4.3 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4d2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 32 11.3 12.4 11.3 12.4
Sometimes 106 37.6 41.1 48.9 53.5
Rarely_Never 107 37.9 41.5 86.9 95.0
Dont_know 13 4.6 5.0 91.5 100.0
NA 24 8.5 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4d3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 103 36.5 39.6 36.5 39.6
Sometimes 86 30.5 33.1 67.0 72.7
Rarely_Never 55 19.5 21.2 86.5 93.8
Dont_know 16 5.7 6.2 92.2 100.0
NA 22 7.8 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

C4E: Ask neighbors for help

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors could you ask for help, such as to “borrow a cup of sugar” or some other small favor?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4e1 <- as.factor(d[,"c4e1"])
# Make "*" to NA
c4e1[which(c4e1=="*")]<-"NA"
  levels(c4e1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e1 <- ordered(c4e1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e1)
  new.d <- apply_labels(new.d, c4e1 = "ask for help-Current")
  temp.d <- data.frame (new.d, c4e1)  
  
  c4e2 <- as.factor(d[,"c4e2"])
# Make "*" to NA
c4e2[which(c4e2=="*")]<-"NA"
  levels(c4e2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e2 <- ordered(c4e2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e2)
  new.d <- apply_labels(new.d, c4e2 = "ask for help-age 31 up")
  temp.d <- data.frame (new.d, c4e2) 
  
  c4e3 <- as.factor(d[,"c4e3"])
  # Make "*" to NA
c4e3[which(c4e3=="*")]<-"NA"
  levels(c4e3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e3 <- ordered(c4e3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e3)
  new.d <- apply_labels(new.d, c4e3 = "ask for help-Childhood or young")
  temp.d <- data.frame (new.d, c4e3)
  
  result<-questionr::freq(temp.d$c4e1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 43 15.2 16.9 15.2 16.9
Sometimes 101 35.8 39.6 51.1 56.5
Rarely_Never 91 32.3 35.7 83.3 92.2
Dont_know 20 7.1 7.8 90.4 100.0
NA 27 9.6 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4e2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 34 12.1 13.9 12.1 13.9
Sometimes 91 32.3 37.1 44.3 51.0
Rarely_Never 95 33.7 38.8 78.0 89.8
Dont_know 25 8.9 10.2 86.9 100.0
NA 37 13.1 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4e3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 85 30.1 34.3 30.1 34.3
Sometimes 78 27.7 31.5 57.8 65.7
Rarely_Never 61 21.6 24.6 79.4 90.3
Dont_know 24 8.5 9.7 87.9 100.0
NA 34 12.1 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

D1: Treat you because of your race/ethnicity

  • D1. In the following questions, we are interested in your perceptions about the way other people have treated you because of your race/ethnicity or skin color.
      1. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
      1. For unfair reasons, have you ever not been hired for a job?
      1. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
      1. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
      1. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
      1. Have you ever been unfairly denied a bank loan?
      1. Have you ever been unfairly treated when getting medical care?
      • 1=No
      • 2=Yes
    • If yes, How stressful was this experience?
      • 1=Not at all
      • 2=A little
      • 3=Somewhat
      • 4=Extremely
# a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
  d1aa <- as.factor(d[,"d1aa"])
# Make "*" to NA
d1aa[which(d1aa=="*")]<-"NA"
  levels(d1aa) <- list(No="1",
                     Yes="2")
  d1aa <- ordered(d1aa, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1aa)
  new.d <- apply_labels(new.d, d1aa = "fired or denied a promotion")
  temp.d <- data.frame (new.d, d1aa)  
  
  d1ab <- as.factor(d[,"d1ab"])
# Make "*" to NA
d1ab[which(d1ab=="*")]<-"NA" 
  levels(d1ab) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1ab <- ordered(d1ab, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ab)
  new.d <- apply_labels(new.d, d1ab = "fired or denied a promotion-stressful")
  temp.d <- data.frame (new.d, d1ab)
  
  result<-questionr::freq(temp.d$d1aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
")
a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
n % val%
No 121 42.9 45
Yes 148 52.5 55
NA 13 4.6 NA
Total 282 100.0 100 
  result<-questionr::freq(temp.d$d1ab,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How stressful was this experience?")
a. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 282 100 NA 100 NA
Total 282 100 100 100 100 
# b. For unfair reasons, have you ever not been hired for a job?
  d1ba <- as.factor(d[,"d1ba"])
  # Make "*" to NA
d1ba[which(d1ba=="*")]<-"NA"
  levels(d1ba) <- list(No="1",
                     Yes="2")
  d1ba <- ordered(d1ba, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ba)
  new.d <- apply_labels(new.d, d1ba = "not be hired")
  temp.d <- data.frame (new.d, d1ba)  
  
  d1bb <- as.factor(d[,"d1bb"])
  # Make "*" to NA
d1bb[which(d1bb=="*")]<-"NA"
  levels(d1bb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1bb <- ordered(d1bb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1bb)
  new.d <- apply_labels(new.d, d1bb = "not be hired-stressful")
  temp.d <- data.frame (new.d, d1bb)
  
  result<-questionr::freq(temp.d$d1ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. For unfair reasons, have you ever not been hired for a job?")
b. For unfair reasons, have you ever not been hired for a job?
n % val%
No 127 45.0 48.3
Yes 136 48.2 51.7
NA 19 6.7 NA
Total 282 100.0 100.0 
  result<-questionr::freq(temp.d$d1bb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How stressful was this experience?")
b. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 282 100 NA 100 NA
Total 282 100 100 100 100 
# c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
  d1ca <- as.factor(d[,"d1ca"])
  # Make "*" to NA
d1ca[which(d1ca=="*")]<-"NA"
  levels(d1ca) <- list(No="1",
                     Yes="2")
  d1ca <- ordered(d1ca, c( "No","Yes"))
  
  new.d <- data.frame(new.d, d1ca)
  new.d <- apply_labels(new.d, d1ca = "By police")
  temp.d <- data.frame (new.d, d1ca)  
  
  result<-questionr::freq(temp.d$d1ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?")
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
n % val%
No 102 36.2 38.1
Yes 166 58.9 61.9
NA 14 5.0 NA
Total 282 100.0 100.0 
  d1cb <- as.factor(d[,"d1cb"])
  # Make "*" to NA
d1cb[which(d1cb=="*")]<-"NA"
  levels(d1cb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1cb <- ordered(d1cb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1cb)
  new.d <- apply_labels(new.d, d1cb = "By police-stressful")
  temp.d <- data.frame (new.d, d1cb)
  result<-questionr::freq(temp.d$d1cb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. If yes, How stressful was this experience?")
c. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 282 100 NA 100 NA
Total 282 100 100 100 100 
# d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
  d1da <- as.factor(d[,"d1da"])
  # Make "*" to NA
d1da[which(d1da=="*")]<-"NA"
  levels(d1da) <- list(No="1",
                     Yes="2")
  d1da <- ordered(d1da, c( "No","Yes"))
  
  new.d <- data.frame(new.d, d1da)
  new.d <- apply_labels(new.d, d1da = "unfair education")
  temp.d <- data.frame (new.d, d1da)  
  
  result<-questionr::freq(temp.d$d1da,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?")
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
n % val%
No 205 72.7 76.2
Yes 64 22.7 23.8
NA 13 4.6 NA
Total 282 100.0 100.0 
  d1db <- as.factor(d[,"d1db"])
  # Make "*" to NA
d1db[which(d1db=="*")]<-"NA"
  levels(d1db) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1db <- ordered(d1db, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1db)
  new.d <- apply_labels(new.d, d1db = "unfair education-stressful")
  temp.d <- data.frame (new.d, d1db)
  result<-questionr::freq(temp.d$d1db,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. If yes, How stressful was this experience?")
d. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 282 100 NA 100 NA
Total 282 100 100 100 100 
# e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
  d1ea <- as.factor(d[,"d1ea"])
  # Make "*" to NA
d1ea[which(d1ea=="*")]<-"NA"
  levels(d1ea) <- list(No="1",
                     Yes="2")
  d1ea <- ordered(d1ea, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ea)
  new.d <- apply_labels(new.d, d1ea = "refuse to sell or rent")
  temp.d <- data.frame (new.d, d1ea)  
  
  result<-questionr::freq(temp.d$d1ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?")
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
n % val%
No 199 70.6 74
Yes 70 24.8 26
NA 13 4.6 NA
Total 282 100.0 100 
  d1eb <- as.factor(d[,"d1eb"])
  # Make "*" to NA
d1eb[which(d1eb=="*")]<-"NA"
  levels(d1eb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1eb <- ordered(d1eb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1eb)
  new.d <- apply_labels(new.d, d1eb = "refuse to sell or rent-stressful")
  temp.d <- data.frame (new.d, d1eb)
  result<-questionr::freq(temp.d$d1eb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. If yes, How stressful was this experience?")
e. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 282 100 NA 100 NA
Total 282 100 100 100 100 
# f.   Have   you   ever   been   unfairly denied a bank loan?
  d1fa <- as.factor(d[,"d1fa"])
  # Make "*" to NA
d1fa[which(d1fa=="*")]<-"NA"
  levels(d1fa) <- list(No="1",
                     Yes="2")
  d1fa <- ordered(d1fa, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1fa)
  new.d <- apply_labels(new.d, d1fa = "Bank loan")
  temp.d <- data.frame (new.d, d1fa)  
  
  result<-questionr::freq(temp.d$d1fa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. Have you ever been unfairly denied a bank loan?")
f. Have you ever been unfairly denied a bank loan?
n % val%
No 192 68.1 71.9
Yes 75 26.6 28.1
NA 15 5.3 NA
Total 282 100.0 100.0 
  d1fb <- as.factor(d[,"d1fb"])
  # Make "*" to NA
d1fb[which(d1fb=="*")]<-"NA"
  levels(d1fb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1fb <- ordered(d1fb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1fb)
  new.d <- apply_labels(new.d, d1fb = "Bank loan-stressful")
  temp.d <- data.frame (new.d, d1fb)
  result<-questionr::freq(temp.d$d1fb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. If yes, How stressful was this experience?")
f. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 282 100 NA 100 NA
Total 282 100 100 100 100 
# g.   Have   you   ever   been   unfairly treated when getting medical care?
  d1ga <- as.factor(d[,"d1ga"])
  # Make "*" to NA
d1ga[which(d1ga=="*")]<-"NA"
  levels(d1ga) <- list(No="1",
                     Yes="2")
  d1ga <- ordered(d1ga, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ga)
  new.d <- apply_labels(new.d, d1ga = "unfair medical care")
  temp.d <- data.frame (new.d, d1ga)  
  
  result<-questionr::freq(temp.d$d1ga,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. Have you ever been unfairly treated when getting medical care?")
g. Have you ever been unfairly treated when getting medical care?
n % val%
No 222 78.7 81.9
Yes 49 17.4 18.1
NA 11 3.9 NA
Total 282 100.0 100.0 
  d1gb <- as.factor(d[,"d1gb"])
  # Make "*" to NA
d1gb[which(d1gb=="*")]<-"NA"
  levels(d1gb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1gb <- ordered(d1gb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1gb)
  new.d <- apply_labels(new.d, d1gb = "unfair medical care-stressful")
  temp.d <- data.frame (new.d, d1gb)
  result<-questionr::freq(temp.d$d1gb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. If yes, How stressful was this experience?")
g. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 282 100 NA 100 NA
Total 282 100 100 100 100

D2: Medical Mistrust

  • D2. These next questions are about your current feelings or perceptions regarding healthcare organizations (places where you might get healthcare, like a hospital or clinic). Indicate your level of agreement or disagreement with each statement.
# a. Patients have sometimes been deceived or misled at hospitals.
  d2a <- as.factor(d[,"d2a"])
# Make "*" to NA
d2a[which(d2a=="*")]<-"NA"
  levels(d2a) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2a <- ordered(d2a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2a)
  new.d <- apply_labels(new.d, d2a = "deceived or misled")
  temp.d <- data.frame (new.d, d2a)  
  
  result<-questionr::freq(temp.d$d2a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Patients have sometimes been deceived or misled at hospitals.")
a. Patients have sometimes been deceived or misled at hospitals.
n % val%
Strongly_Agree 43 15.2 16.1
Somewhat_Agree 103 36.5 38.6
Somewhat_Disagree 63 22.3 23.6
Strongly_Disagree 58 20.6 21.7
NA 15 5.3 NA
Total 282 100.0 100.0 
# b. Hospitals often want to know more about your personal affairs or business than they really need to know.
  d2b <- as.factor(d[,"d2b"])
# Make "*" to NA
d2b[which(d2b=="*")]<-"NA"
  levels(d2b) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2b <- ordered(d2b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2b)
  new.d <- apply_labels(new.d, d2b = "personal affairs")
  temp.d <- data.frame (new.d, d2b)  
  
  result<-questionr::freq(temp.d$d2b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Hospitals often want to know more about your personal affairs or business than they really need to know.")
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
n % val%
Strongly_Agree 24 8.5 9.0
Somewhat_Agree 77 27.3 28.8
Somewhat_Disagree 100 35.5 37.5
Strongly_Disagree 66 23.4 24.7
NA 15 5.3 NA
Total 282 100.0 100.0 
# c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
  d2c <- as.factor(d[,"d2c"])
# Make "*" to NA
d2c[which(d2c=="*")]<-"NA"
  levels(d2c) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2c <- ordered(d2c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2c)
  new.d <- apply_labels(new.d, d2c = "harmful experiments")
  temp.d <- data.frame (new.d, d2c)  
  
  result<-questionr::freq(temp.d$d2c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Hospitals have sometimes done harmful experiments on patients without their knowledge.")
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
n % val%
Strongly_Agree 41 14.5 16.0
Somewhat_Agree 87 30.9 33.9
Somewhat_Disagree 60 21.3 23.3
Strongly_Disagree 69 24.5 26.8
NA 25 8.9 NA
Total 282 100.0 100.0 
# d. Rich patients receive better care at hospitals than poor patients.
  d2d <- as.factor(d[,"d2d"])
# Make "*" to NA
d2d[which(d2d=="*")]<-"NA"
  levels(d2d) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2d <- ordered(d2d, c( "Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2d)
  new.d <- apply_labels(new.d, d2d = "Rich patients better care")
  temp.d <- data.frame (new.d, d2d)  
  
  result<-questionr::freq(temp.d$d2d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Rich patients receive better care at hospitals than poor patients.")
d. Rich patients receive better care at hospitals than poor patients.
n % val%
Strongly_Agree 147 52.1 55.9
Somewhat_Agree 69 24.5 26.2
Somewhat_Disagree 26 9.2 9.9
Strongly_Disagree 21 7.4 8.0
NA 19 6.7 NA
Total 282 100.0 100.0 
# e. Male patients receive better care at hospitals than female patients.
  d2e <- as.factor(d[,"d2e"])
# Make "*" to NA
d2e[which(d2e=="*")]<-"NA"
  levels(d2e) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2e <- ordered(d2e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2e)
  new.d <- apply_labels(new.d, d2e = "Male patients better care")
  temp.d <- data.frame (new.d, d2e)  
  
  result<-questionr::freq(temp.d$d2e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Male patients receive better care at hospitals than female patients.")
e. Male patients receive better care at hospitals than female patients.
n % val%
Strongly_Agree 13 4.6 5.0
Somewhat_Agree 43 15.2 16.5
Somewhat_Disagree 117 41.5 44.8
Strongly_Disagree 88 31.2 33.7
NA 21 7.4 NA
Total 282 100.0 100.0

D3A: Treated with less respect

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been treated with less respect than other people
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3a1 <- as.factor(d[,"d3a1"])
# Make "*" to NA
d3a1[which(d3a1=="*")]<-"NA"
  levels(d3a1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a1 <- ordered(d3a1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a1)
  new.d <- apply_labels(new.d, d3a1 = "less respect-current")
  temp.d <- data.frame (new.d, d3a1)  
  
  result<-questionr::freq(temp.d$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 64 22.7 23.8
Rarely 74 26.2 27.5
Sometimes 113 40.1 42.0
Often 18 6.4 6.7
NA 13 4.6 NA
Total 282 100.0 100.0 
#2
  d3a2 <- as.factor(d[,"d3a2"])
# Make "*" to NA
d3a2[which(d3a2=="*")]<-"NA"
  levels(d3a2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a2 <- ordered(d3a2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a2)
  new.d <- apply_labels(new.d, d3a2 = "less respect-31 up")
  temp.d <- data.frame (new.d, d3a2)  
  
  result<-questionr::freq(temp.d$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 47 16.7 18.1
Rarely 64 22.7 24.6
Sometimes 122 43.3 46.9
Often 27 9.6 10.4
NA 22 7.8 NA
Total 282 100.0 100.0 
#3
  d3a3 <- as.factor(d[,"d3a3"])
  # Make "*" to NA
d3a3[which(d3a3=="*")]<-"NA"
  levels(d3a3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a3 <- ordered(d3a3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a3)
  new.d <- apply_labels(new.d, d3a3 = "less respect-child or young")
  temp.d <- data.frame (new.d, d3a3)  
  
  result<-questionr::freq(temp.d$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 46 16.3 17.8
Rarely 53 18.8 20.5
Sometimes 103 36.5 39.8
Often 57 20.2 22.0
NA 23 8.2 NA
Total 282 100.0 100.0

D3B: Received poorer service

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have received poorer service than other people at restaurants or stores
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3b1 <- as.factor(d[,"d3b1"])
# Make "*" to NA
d3b1[which(d3b1=="*")]<-"NA"
  levels(d3b1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b1 <- ordered(d3b1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b1)
  new.d <- apply_labels(new.d, d3b1 = "poorer service-current")
  temp.d <- data.frame (new.d, d3b1)  
  
  result<-questionr::freq(temp.d$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 60 21.3 22.6
Rarely 81 28.7 30.5
Sometimes 112 39.7 42.1
Often 13 4.6 4.9
NA 16 5.7 NA
Total 282 100.0 100.0 
#2
  d3b2 <- as.factor(d[,"d3b2"])
  # Make "*" to NA
d3b2[which(d3b2=="*")]<-"NA"
  levels(d3b2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b2 <- ordered(d3b2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b2)
  new.d <- apply_labels(new.d, d3b2 = "poorer service-31 up")
  temp.d <- data.frame (new.d, d3b2)  
  
  result<-questionr::freq(temp.d$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 43 15.2 16.5
Rarely 77 27.3 29.6
Sometimes 123 43.6 47.3
Often 17 6.0 6.5
NA 22 7.8 NA
Total 282 100.0 100.0 
#3
  d3b3 <- as.factor(d[,"d3b3"])
  # Make "*" to NA
d3b3[which(d3b3=="*")]<-"NA"
  levels(d3b3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b3 <- ordered(d3b3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b3)
  new.d <- apply_labels(new.d, d3b3 = "poorer service-child or young")
  temp.d <- data.frame (new.d, d3b3)  
  
  result<-questionr::freq(temp.d$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 49 17.4 19.1
Rarely 51 18.1 19.8
Sometimes 107 37.9 41.6
Often 50 17.7 19.5
NA 25 8.9 NA
Total 282 100.0 100.0

D3C: Think you are not smart

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they think you are not smart
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3c1 <- as.factor(d[,"d3c1"])
# Make "*" to NA
d3c1[which(d3c1=="*")]<-"NA"
  levels(d3c1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c1 <- ordered(d3c1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c1)
  new.d <- apply_labels(new.d, d3c1 = "think you are not smart-current")
  temp.d <- data.frame (new.d, d3c1)  
  
  result<-questionr::freq(temp.d$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 75 26.6 28.4
Rarely 78 27.7 29.5
Sometimes 92 32.6 34.8
Often 19 6.7 7.2
NA 18 6.4 NA
Total 282 100.0 100.0 
#2
  d3c2 <- as.factor(d[,"d3c2"])
# Make "*" to NA
d3c2[which(d3c2=="*")]<-"NA"
  levels(d3c2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c2 <- ordered(d3c2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c2)
  new.d <- apply_labels(new.d, d3c2 = "think you are not smart-31 up")
  temp.d <- data.frame (new.d, d3c2)  
  
  result<-questionr::freq(temp.d$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 56 19.9 21.9
Rarely 67 23.8 26.2
Sometimes 112 39.7 43.8
Often 21 7.4 8.2
NA 26 9.2 NA
Total 282 100.0 100.0 
#3
  d3c3 <- as.factor(d[,"d3c3"])
  # Make "*" to NA
d3c3[which(d3c3=="*")]<-"NA"
  levels(d3c3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c3 <- ordered(d3c3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c3)
  new.d <- apply_labels(new.d, d3c3 = "think you are not smart-child or young")
  temp.d <- data.frame (new.d, d3c3)  
  
  result<-questionr::freq(temp.d$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 56 19.9 22.0
Rarely 55 19.5 21.6
Sometimes 96 34.0 37.6
Often 48 17.0 18.8
NA 27 9.6 NA
Total 282 100.0 100.0

D3D: Be afraid of you

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they are afraid of you
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3d1 <- as.factor(d[,"d3d1"])
# Make "*" to NA
d3d1[which(d3d1=="*")]<-"NA"
  levels(d3d1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d1 <- ordered(d3d1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d1)
  new.d <- apply_labels(new.d, d3d1 = "be afraid of you-current")
  temp.d <- data.frame (new.d, d3d1)  
  
  result<-questionr::freq(temp.d$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 76 27.0 28.3
Rarely 83 29.4 30.9
Sometimes 90 31.9 33.5
Often 20 7.1 7.4
NA 13 4.6 NA
Total 282 100.0 100.0 
#2
  d3d2 <- as.factor(d[,"d3d2"])
  # Make "*" to NA
d3d2[which(d3d2=="*")]<-"NA"
  levels(d3d2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d2 <- ordered(d3d2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d2)
  new.d <- apply_labels(new.d, d3d2 = "be afraid of you-31 up")
  temp.d <- data.frame (new.d, d3d2)  
  
  result<-questionr::freq(temp.d$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 65 23.0 25.1
Rarely 66 23.4 25.5
Sometimes 96 34.0 37.1
Often 32 11.3 12.4
NA 23 8.2 NA
Total 282 100.0 100.0 
#3
  d3d3 <- as.factor(d[,"d3d3"])
  # Make "*" to NA
d3d3[which(d3d3=="*")]<-"NA"
  levels(d3d3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d3 <- ordered(d3d3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d3)
  new.d <- apply_labels(new.d, d3d3 = "be afraid of you-child or young")
  temp.d <- data.frame (new.d, d3d3)  
  
  result<-questionr::freq(temp.d$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 69 24.5 26.8
Rarely 57 20.2 22.2
Sometimes 89 31.6 34.6
Often 42 14.9 16.3
NA 25 8.9 NA
Total 282 100.0 100.0

D3E: Think you are dishonest

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they think you are dishonest
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3e1 <- as.factor(d[,"d3e1"])
# Make "*" to NA
d3e1[which(d3e1=="*")]<-"NA"
  levels(d3e1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e1 <- ordered(d3e1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e1)
  new.d <- apply_labels(new.d, d3e1 = "think you are dishonest-current")
  temp.d <- data.frame (new.d, d3e1)  
  
  result<-questionr::freq(temp.d$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 107 37.9 40.1
Rarely 84 29.8 31.5
Sometimes 67 23.8 25.1
Often 9 3.2 3.4
NA 15 5.3 NA
Total 282 100.0 100.0 
#2
  d3e2 <- as.factor(d[,"d3e2"])
  # Make "*" to NA
d3e2[which(d3e2=="*")]<-"NA"
  levels(d3e2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e2 <- ordered(d3e2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e2)
  new.d <- apply_labels(new.d, d3e2 = "think you are dishonest-31 up")
  temp.d <- data.frame (new.d, d3e2)  
  
  result<-questionr::freq(temp.d$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 80 28.4 31.1
Rarely 75 26.6 29.2
Sometimes 83 29.4 32.3
Often 19 6.7 7.4
NA 25 8.9 NA
Total 282 100.0 100.0 
#3
  d3e3 <- as.factor(d[,"d3e3"])
  # Make "*" to NA
d3e3[which(d3e3=="*")]<-"NA"
  levels(d3e3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e3 <- ordered(d3e3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e3)
  new.d <- apply_labels(new.d, d3e3 = "think you are dishonest-child or young")
  temp.d <- data.frame (new.d, d3e3)  
  
  result<-questionr::freq(temp.d$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 75 26.6 29.4
Rarely 66 23.4 25.9
Sometimes 87 30.9 34.1
Often 27 9.6 10.6
NA 27 9.6 NA
Total 282 100.0 100.0

D3F: Better than you

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they’re better than you are
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3f1 <- as.factor(d[,"d3f1"])
# Make "*" to NA
d3f1[which(d3f1=="*")]<-"NA"
  levels(d3f1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f1 <- ordered(d3f1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f1)
  new.d <- apply_labels(new.d, d3f1 = "better than you-current")
  temp.d <- data.frame (new.d, d3f1)  
  
  result<-questionr::freq(temp.d$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 41 14.5 15.4
Rarely 73 25.9 27.4
Sometimes 113 40.1 42.5
Often 39 13.8 14.7
NA 16 5.7 NA
Total 282 100.0 100.0 
#2
  d3f2 <- as.factor(d[,"d3f2"])
  # Make "*" to NA
d3f2[which(d3f2=="*")]<-"NA"
  levels(d3f2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f2 <- ordered(d3f2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f2)
  new.d <- apply_labels(new.d, d3f2 = "better than you-31 up")
  temp.d <- data.frame (new.d, d3f2)  
  
  result<-questionr::freq(temp.d$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 28 9.9 10.9
Rarely 57 20.2 22.2
Sometimes 127 45.0 49.4
Often 45 16.0 17.5
NA 25 8.9 NA
Total 282 100.0 100.0 
#3
  d3f3 <- as.factor(d[,"d3f3"])
# Make "*" to NA
d3f3[which(d3f3=="*")]<-"NA"
  levels(d3f3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f3 <- ordered(d3f3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f3)
  new.d <- apply_labels(new.d, d3f3 = "better than you-child or young")
  temp.d <- data.frame (new.d, d3f3)  
  
  result<-questionr::freq(temp.d$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 28 9.9 11.0
Rarely 57 20.2 22.4
Sometimes 101 35.8 39.6
Often 69 24.5 27.1
NA 27 9.6 NA
Total 282 100.0 100.0

D3G: Insulted

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been called names or insulted
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3g1 <- as.factor(d[,"d3g1"])
# Make "*" to NA
d3g1[which(d3g1=="*")]<-"NA"
  levels(d3g1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g1 <- ordered(d3g1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g1)
  new.d <- apply_labels(new.d, d3g1 = "called names or insulted-current")
  temp.d <- data.frame (new.d, d3g1)  
  
  result<-questionr::freq(temp.d$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 88 31.2 32.7
Rarely 106 37.6 39.4
Sometimes 66 23.4 24.5
Often 9 3.2 3.3
NA 13 4.6 NA
Total 282 100.0 100.0 
#2
  d3g2 <- as.factor(d[,"d3g2"])
  # Make "*" to NA
d3g2[which(d3g2=="*")]<-"NA"
  levels(d3g2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g2 <- ordered(d3g2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g2)
  new.d <- apply_labels(new.d, d3g2 = "called names or insulted-31 up")
  temp.d <- data.frame (new.d, d3g2)  
  
  result<-questionr::freq(temp.d$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 52 18.4 19.9
Rarely 96 34.0 36.8
Sometimes 98 34.8 37.5
Often 15 5.3 5.7
NA 21 7.4 NA
Total 282 100.0 100.0 
#3
  d3g3 <- as.factor(d[,"d3g3"])
  # Make "*" to NA
d3g3[which(d3g3=="*")]<-"NA"
  levels(d3g3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g3 <- ordered(d3g3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g3)
  new.d <- apply_labels(new.d, d3g3 = "called names or insulted-child or young")
  temp.d <- data.frame (new.d, d3g3)  
  
  result<-questionr::freq(temp.d$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 40 14.2 15.4
Rarely 55 19.5 21.2
Sometimes 115 40.8 44.4
Often 49 17.4 18.9
NA 23 8.2 NA
Total 282 100.0 100.0

D3H: Threatened or harassed

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been threatened or harassed
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3h1 <- as.factor(d[,"d3h1"])
# Make "*" to NA
d3h1[which(d3h1=="*")]<-"NA"
  levels(d3h1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h1 <- ordered(d3h1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h1)
  new.d <- apply_labels(new.d, d3h1 = "threatened or harassed-current")
  temp.d <- data.frame (new.d, d3h1)  
  
  result<-questionr::freq(temp.d$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 140 49.6 52.4
Rarely 87 30.9 32.6
Sometimes 34 12.1 12.7
Often 6 2.1 2.2
NA 15 5.3 NA
Total 282 100.0 100.0 
#2
  d3h2 <- as.factor(d[,"d3h2"])
  # Make "*" to NA
d3h2[which(d3e1=="*")]<-"NA"
  levels(d3h2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h2 <- ordered(d3h2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h2)
  new.d <- apply_labels(new.d, d3h2 = "threatened or harassed-31 up")
  temp.d <- data.frame (new.d, d3h2)  
  
  result<-questionr::freq(temp.d$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 100 35.5 38.6
Rarely 96 34.0 37.1
Sometimes 56 19.9 21.6
Often 7 2.5 2.7
NA 23 8.2 NA
Total 282 100.0 100.0 
#3
  d3h3 <- as.factor(d[,"d3h3"])
  # Make "*" to NA
d3h3[which(d3h3=="*")]<-"NA"
  levels(d3h3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h3 <- ordered(d3h3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h3)
  new.d <- apply_labels(new.d, d3h3 = "threatened or harassed-child or young")
  temp.d <- data.frame (new.d, d3h3)  
  
  result<-questionr::freq(temp.d$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 70 24.8 27.1
Rarely 76 27.0 29.5
Sometimes 82 29.1 31.8
Often 30 10.6 11.6
NA 24 8.5 NA
Total 282 100.0 100.0

D3I: Followed around in stores

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been followed around in stores
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3i1 <- as.factor(d[,"d3i1"])
# Make "*" to NA
d3i1[which(d3e1=="*")]<-"NA"
  levels(d3i1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i1 <- ordered(d3i1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i1)
  new.d <- apply_labels(new.d, d3i1 = "be followed-current")
  temp.d <- data.frame (new.d, d3i1)  
  
  result<-questionr::freq(temp.d$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 92 32.6 34.5
Rarely 84 29.8 31.5
Sometimes 67 23.8 25.1
Often 24 8.5 9.0
NA 15 5.3 NA
Total 282 100.0 100.0 
#2
  d3i2 <- as.factor(d[,"d3i2"])
  # Make "*" to NA
d3i1[which(d3i1=="*")]<-"NA"
  levels(d3i2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i2 <- ordered(d3i2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i2)
  new.d <- apply_labels(new.d, d3i2 = "be followed-31 up")
  temp.d <- data.frame (new.d, d3i2)  
  
  result<-questionr::freq(temp.d$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 58 20.6 22.4
Rarely 78 27.7 30.1
Sometimes 89 31.6 34.4
Often 34 12.1 13.1
NA 23 8.2 NA
Total 282 100.0 100.0 
#3
  d3i3 <- as.factor(d[,"d3i3"])
  # Make "*" to NA
d3i1[which(d3i1=="*")]<-"NA"
  levels(d3i3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i3 <- ordered(d3i3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i3)
  new.d <- apply_labels(new.d, d3i3 = "be followed-child or young")
  temp.d <- data.frame (new.d, d3i3)  
  
  result<-questionr::freq(temp.d$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 56 19.9 21.6
Rarely 46 16.3 17.8
Sometimes 87 30.9 33.6
Often 70 24.8 27.0
NA 23 8.2 NA
Total 282 100.0 100.0

D3J: How stressful

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. How stressful has any of the above experience (a-i) of unfair treatment usually been for you?
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3j1 <- as.factor(d[,"d3j1"])
# Make "*" to NA
d3j1[which(d3j1=="*")]<-"NA"
  levels(d3j1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j1 <- ordered(d3j1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j1)
  new.d <- apply_labels(new.d, d3j1 = "How stressful-current")
  temp.d <- data.frame (new.d, d3j1)  
  
  result<-questionr::freq(temp.d$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 94 33.3 35.3
Rarely 97 34.4 36.5
Sometimes 56 19.9 21.1
Often 19 6.7 7.1
NA 16 5.7 NA
Total 282 100.0 100.0 
#2
  d3j2 <- as.factor(d[,"d3j2"])
  # Make "*" to NA
d3j2[which(d3j2=="*")]<-"NA"
  levels(d3j2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j2 <- ordered(d3j2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j2)
  new.d <- apply_labels(new.d, d3j2 = "How stressful-31 up")
  temp.d <- data.frame (new.d, d3j2)  
  
  result<-questionr::freq(temp.d$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 67 23.8 26.0
Rarely 97 34.4 37.6
Sometimes 66 23.4 25.6
Often 28 9.9 10.9
NA 24 8.5 NA
Total 282 100.0 100.0 
#3
  d3j3 <- as.factor(d[,"d3j3"])
  # Make "*" to NA
d3j3[which(d3j3=="*")]<-"NA"
  levels(d3j3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j3 <- ordered(d3j3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j3)
  new.d <- apply_labels(new.d, d3j3 = "How stressful-child or young")
  temp.d <- data.frame (new.d, d3j3)  
  
  result<-questionr::freq(temp.d$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 62 22.0 24.2
Rarely 78 27.7 30.5
Sometimes 64 22.7 25.0
Often 52 18.4 20.3
NA 26 9.2 NA
Total 282 100.0 100.0

D4: How you currently see yourself

  • D4. These statements are about how you currently see yourself. Indicate your level of agreement or disagreement with each statement.
      1. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
      1. Once you make up your mind to do something, you stay with it until the job is completely done.
      1. You like doing things that other people thought could not be done.
      1. When things don’t go the way you want them to, that just makes you work even harder.
      1. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
      1. It’s not always easy, but you manage to find a way to do the things you really need to get done.
      1. Very seldom have you been disappointed by the results of your hard work.
      1. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
      1. In the past, even when things got really tough, you never lost sight of your goals.
      1. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
      1. You don’t let your personal feelings get in the way of doing a job.
      1. Hard work has really helped you to get ahead in life.
      • 1=Strongly Agree
      • 2=Somewhat Agree
      • 3=Somewhat Disagree
      • 4=Strongly Disagree
# a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
  d4a <- as.factor(d[,"d4a"])
# Make "*" to NA
d4a[which(d4a=="*")]<-"NA"
  levels(d4a) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4a <- ordered(d4a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4a)
  new.d <- apply_labels(new.d, d4a = "make life")
  temp.d <- data.frame (new.d, d4a)  
  
  result<-questionr::freq(temp.d$d4a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.")
a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
n % val% %cum val%cum
Strongly_Agree 131 46.5 48.5 46.5 48.5
Somewhat_Agree 105 37.2 38.9 83.7 87.4
Somewhat_Disagree 29 10.3 10.7 94.0 98.1
Strongly_Disagree 5 1.8 1.9 95.7 100.0
NA 12 4.3 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
# b. Once you make up your mind to do something, you stay with it until the job is completely done.
  d4b <- as.factor(d[,"d4b"])
  # Make "*" to NA
d4b[which(d4b=="*")]<-"NA"
  levels(d4b) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4b <- ordered(d4b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4b)
  new.d <- apply_labels(new.d, d4b = "until job is done")
  temp.d <- data.frame (new.d, d4b)  
  
  result<-questionr::freq(temp.d$d4b,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Once you make up your mind to do something, you stay with it until the job is completely done.")
b. Once you make up your mind to do something, you stay with it until the job is completely done.
n % val% %cum val%cum
Strongly_Agree 162 57.4 60.0 57.4 60.0
Somewhat_Agree 89 31.6 33.0 89.0 93.0
Somewhat_Disagree 16 5.7 5.9 94.7 98.9
Strongly_Disagree 3 1.1 1.1 95.7 100.0
NA 12 4.3 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
# c. You like doing things that other people thought could not be done.
  d4c <- as.factor(d[,"d4c"])
  # Make "*" to NA
d4c[which(d4c=="*")]<-"NA"
  levels(d4c) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4c <- ordered(d4c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4c)
  new.d <- apply_labels(new.d, d4c = "until job is done")
  temp.d <- data.frame (new.d, d4c)  
  
  result<-questionr::freq(temp.d$d4c,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. You like doing things that other people thought could not be done.")
c. You like doing things that other people thought could not be done.
n % val% %cum val%cum
Strongly_Agree 120 42.6 45.1 42.6 45.1
Somewhat_Agree 116 41.1 43.6 83.7 88.7
Somewhat_Disagree 24 8.5 9.0 92.2 97.7
Strongly_Disagree 6 2.1 2.3 94.3 100.0
NA 16 5.7 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
# d. When things don’t go the way you want them to, that just makes you work even harder.
  d4d <- as.factor(d[,"d4d"])
  # Make "*" to NA
d4d[which(d4d=="*")]<-"NA"
  levels(d4d) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4d <- ordered(d4d, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4d)
  new.d <- apply_labels(new.d, d4d = "until job is done")
  temp.d <- data.frame (new.d, d4d)  
  
  result<-questionr::freq(temp.d$d4d,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. When things don’t go the way you want them to, that just makes you work even harder.")
d. When things don’t go the way you want them to, that just makes you work even harder.
n % val% %cum val%cum
Strongly_Agree 123 43.6 45.6 43.6 45.6
Somewhat_Agree 122 43.3 45.2 86.9 90.7
Somewhat_Disagree 20 7.1 7.4 94.0 98.1
Strongly_Disagree 5 1.8 1.9 95.7 100.0
NA 12 4.3 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
# e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
  d4e <- as.factor(d[,"d4e"])
  # Make "*" to NA
d4e[which(d4e=="*")]<-"NA"
  levels(d4e) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4e <- ordered(d4e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4e)
  new.d <- apply_labels(new.d, d4e = "do it yourself")
  temp.d <- data.frame (new.d, d4e)  
  
  result<-questionr::freq(temp.d$d4e,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.")
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
n % val% %cum val%cum
Strongly_Agree 103 36.5 38.3 36.5 38.3
Somewhat_Agree 114 40.4 42.4 77.0 80.7
Somewhat_Disagree 44 15.6 16.4 92.6 97.0
Strongly_Disagree 8 2.8 3.0 95.4 100.0
NA 13 4.6 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
# f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
  d4f <- as.factor(d[,"d4f"])
  # Make "*" to NA
d4f[which(d4f=="*")]<-"NA"
  levels(d4f) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4f <- ordered(d4f, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4f)
  new.d <- apply_labels(new.d, d4f = "not easy but get it done")
  temp.d <- data.frame (new.d, d4f)  
  
  result<-questionr::freq(temp.d$d4f,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. It’s not always easy, but you manage to find a way to do the things you really need to get done.")
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
n % val% %cum val%cum
Strongly_Agree 163 57.8 60.4 57.8 60.4
Somewhat_Agree 102 36.2 37.8 94.0 98.1
Somewhat_Disagree 5 1.8 1.9 95.7 100.0
Strongly_Disagree 0 0.0 0.0 95.7 100.0
NA 12 4.3 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
# g. Very seldom have you been disappointed by the results of your hard work.
  d4g <- as.factor(d[,"d4g"])
  # Make "*" to NA
d4g[which(d4g=="*")]<-"NA"
  levels(d4g) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4g <- ordered(d4g, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4g)
  new.d <- apply_labels(new.d, d4g = "seldom disappointed")
  temp.d <- data.frame (new.d, d4g)  
  
  result<-questionr::freq(temp.d$d4g,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. Very seldom have you been disappointed by the results of your hard work.")
g. Very seldom have you been disappointed by the results of your hard work.
n % val% %cum val%cum
Strongly_Agree 97 34.4 36.1 34.4 36.1
Somewhat_Agree 122 43.3 45.4 77.7 81.4
Somewhat_Disagree 44 15.6 16.4 93.3 97.8
Strongly_Disagree 6 2.1 2.2 95.4 100.0
NA 13 4.6 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
# h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
  d4h <- as.factor(d[,"d4h"])
  # Make "*" to NA
d4h[which(d4h=="*")]<-"NA"
  levels(d4h) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4h <- ordered(d4h, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4h)
  new.d <- apply_labels(new.d, d4h = "stand up for believes")
  temp.d <- data.frame (new.d, d4h)  
  
  result<-questionr::freq(temp.d$d4h,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.")
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
n % val% %cum val%cum
Strongly_Agree 163 57.8 60.4 57.8 60.4
Somewhat_Agree 91 32.3 33.7 90.1 94.1
Somewhat_Disagree 13 4.6 4.8 94.7 98.9
Strongly_Disagree 3 1.1 1.1 95.7 100.0
NA 12 4.3 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
# i. In the past, even when things got really tough, you never lost sight of your goals.
  d4i <- as.factor(d[,"d4i"])
    # Make "*" to NA
d4i[which(d4i=="*")]<-"NA"
  levels(d4i) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4i <- ordered(d4i, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4i)
  new.d <- apply_labels(new.d, d4i = "tough but never lost")
  temp.d <- data.frame (new.d, d4i)  
  
  result<-questionr::freq(temp.d$d4i,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "i. In the past, even when things got really tough, you never lost sight of your goals.")
i. In the past, even when things got really tough, you never lost sight of your goals.
n % val% %cum val%cum
Strongly_Agree 157 55.7 58.1 55.7 58.1
Somewhat_Agree 94 33.3 34.8 89.0 93.0
Somewhat_Disagree 18 6.4 6.7 95.4 99.6
Strongly_Disagree 1 0.4 0.4 95.7 100.0
NA 12 4.3 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
#j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
  d4j <- as.factor(d[,"d4j"])
    # Make "*" to NA
d4j[which(d4j=="*")]<-"NA"
  levels(d4j) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4j <- ordered(d4j, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4j)
  new.d <- apply_labels(new.d, d4j = "the way you want to do matters")
  temp.d <- data.frame (new.d, d4j)  
  
  result<-questionr::freq(temp.d$d4j,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.")
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
n % val% %cum val%cum
Strongly_Agree 79 28.0 29.4 28.0 29.4
Somewhat_Agree 105 37.2 39.0 65.2 68.4
Somewhat_Disagree 72 25.5 26.8 90.8 95.2
Strongly_Disagree 13 4.6 4.8 95.4 100.0
NA 13 4.6 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
#k. You don’t let your personal feelings get in the way of doing a job.
  d4k <- as.factor(d[,"d4k"])
    # Make "*" to NA
d4k[which(d4k=="*")]<-"NA"
  levels(d4k) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4k <- ordered(d4k, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4k)
  new.d <- apply_labels(new.d, d4k = "personal feelings never get in the way of job")
  temp.d <- data.frame (new.d, d4k)  
  
  result<-questionr::freq(temp.d$d4k,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "k. You don’t let your personal feelings get in the way of doing a job.")
k. You don’t let your personal feelings get in the way of doing a job.
n % val% %cum val%cum
Strongly_Agree 135 47.9 50.4 47.9 50.4
Somewhat_Agree 105 37.2 39.2 85.1 89.6
Somewhat_Disagree 25 8.9 9.3 94.0 98.9
Strongly_Disagree 3 1.1 1.1 95.0 100.0
NA 14 5.0 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
#l. Hard work has really helped you to get ahead in life.
  d4l <- as.factor(d[,"d4l"])
    # Make "*" to NA
d4l[which(d4l=="*")]<-"NA"
  levels(d4l) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4l <- ordered(d4l, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4l)
  new.d <- apply_labels(new.d, d4l = "hard work helps")
  temp.d <- data.frame (new.d, d4l)  
  
  result<-questionr::freq(temp.d$d4l,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "l. Hard work has really helped you to get ahead in life.")
l. Hard work has really helped you to get ahead in life.
n % val% %cum val%cum
Strongly_Agree 183 64.9 67.8 64.9 67.8
Somewhat_Agree 65 23.0 24.1 87.9 91.9
Somewhat_Disagree 17 6.0 6.3 94.0 98.1
Strongly_Disagree 5 1.8 1.9 95.7 100.0
NA 12 4.3 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

D5: Childhood

  • D5. The next questions are about the time period of your childhood, before the age of 18. These are standard questions asked in many surveys of life history. This information will allow us to understand how problems that may occur early in life may affect health later in life. This is a sensitive topic and some people may feel uncomfortable with these questions. Please keep in mind that you can skip any question you do not want to answer. All information is kept confidential. When you were growing up, during the first 18 years of your life…
    1. Did you live with anyone who was depressed, mentally ill, or suicidal?
    1. Did you live with anyone who was a problem drinker or alcoholic?
    1. Did you live with anyone who used illegal street drugs or who abused prescription medications?
    1. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
    1. Were your parents separated or divorced?
    1. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
    1. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way? Do not include spanking.
    1. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
    1. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
    1. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
    1. How often did anyone at least 5 years older than you or an adult, force you to have sex?
    • 1=No
    • 2=Yes
    • 3=Parents not married
    • 88=Don’t know/not sure
    • 99=Prefer not to answer”
# a. Did you live with anyone who was depressed, mentally ill, or suicidal?
  d5a <- as.factor(d[,"d5a"])
  # Make "*" to NA
d5a[which(d5a=="*")]<-"NA"
  levels(d5a) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5a <- ordered(d5a, c("No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5a)
  new.d <- apply_labels(new.d, d5a = "live with depressed")
  temp.d <- data.frame (new.d, d5a)  
  
  result<-questionr::freq(temp.d$d5a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you live with anyone who was depressed, mentally ill, or suicidal?")
a. Did you live with anyone who was depressed, mentally ill, or suicidal?
n % val%
No 221 78.4 82.2
Yes 26 9.2 9.7
Dont_know_not_sure 20 7.1 7.4
Prefer_not_to_answer 2 0.7 0.7
NA 13 4.6 NA
Total 282 100.0 100.0 
# b. Did you live with anyone who was a problem drinker or alcoholic?
  d5b <- as.factor(d[,"d5b"])
# Make "*" to NA
d5b[which(d5b=="*")]<-"NA"
  levels(d5b) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5b <- ordered(d5b, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5b)
  new.d <- apply_labels(new.d, d5b = "live with alcoholic")
  temp.d <- data.frame (new.d, d5b)  
  
  result<-questionr::freq(temp.d$d5b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you live with anyone who was a problem drinker or alcoholic?")
b. Did you live with anyone who was a problem drinker or alcoholic?
n % val%
No 177 62.8 65.6
Yes 77 27.3 28.5
Dont_know_not_sure 15 5.3 5.6
Prefer_not_to_answer 1 0.4 0.4
NA 12 4.3 NA
Total 282 100.0 100.0 
# c. Did you live with anyone who used illegal street drugs or who abused prescription medications?  
  d5c <- as.factor(d[,"d5c"])
# Make "*" to NA
d5c[which(d5c=="*")]<-"NA"
  levels(d5c) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5c <- ordered(d5c, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5c)
  new.d <- apply_labels(new.d, d5c = "live with illegal street drugs")
  temp.d <- data.frame (new.d, d5c)  
  
  result<-questionr::freq(temp.d$d5c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Did you live with anyone who used illegal street drugs or who abused prescription medications?")
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
n % val%
No 224 79.4 82.7
Yes 36 12.8 13.3
Dont_know_not_sure 10 3.5 3.7
Prefer_not_to_answer 1 0.4 0.4
NA 11 3.9 NA
Total 282 100.0 100.0 
# d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility? 
  d5d <- as.factor(d[,"d5d"])
# Make "*" to NA
d5d[which(d5d=="*")]<-"NA"
  levels(d5d) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5d <- ordered(d5d, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5d)
  new.d <- apply_labels(new.d, d5d = "live with people in a prison")
  temp.d <- data.frame (new.d, d5d)  
  
  result<-questionr::freq(temp.d$d5d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?")
d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?
n % val%
No 229 81.2 84.8
Yes 36 12.8 13.3
Dont_know_not_sure 3 1.1 1.1
Prefer_not_to_answer 2 0.7 0.7
NA 12 4.3 NA
Total 282 100.0 100.0 
# e. Were your parents separated or divorced? 
  d5e <- as.factor(d[,"d5e"])
# Make "*" to NA
d5e[which(d5e=="*")]<-"NA"
  levels(d5e) <- list(No="1",
                     Yes="2",
                     Not_married="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5e <- ordered(d5e, c( "No","Yes","Not_married","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5e)
  new.d <- apply_labels(new.d, d5e = "parents divorced")
  temp.d <- data.frame (new.d, d5e)  
  
  result<-questionr::freq(temp.d$d5e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Were your parents separated or divorced?")
e. Were your parents separated or divorced?
n % val%
No 153 54.3 56.7
Yes 91 32.3 33.7
Not_married 17 6.0 6.3
Dont_know_not_sure 3 1.1 1.1
Prefer_not_to_answer 6 2.1 2.2
NA 12 4.3 NA
Total 282 100.0 100.0 
# f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
  d5f <- as.factor(d[,"d5f"])
# Make "*" to NA
d5f[which(d5f=="*")]<-"NA"
  levels(d5f) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5f <- ordered(d5f, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5f)
  new.d <- apply_labels(new.d, d5f = "violence to each other")
  temp.d <- data.frame (new.d, d5f)  
  
  result<-questionr::freq(temp.d$d5f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?")
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
n % val%
Never 156 55.3 57.8
Once 22 7.8 8.1
More_than_once 54 19.1 20.0
Dont_know_not_sure 27 9.6 10.0
Prefer_not_to_answer 11 3.9 4.1
NA 12 4.3 NA
Total 282 100.0 100.0 
#  g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
  d5g <- as.factor(d[,"d5g"])
# Make "*" to NA
d5g[which(d5g=="*")]<-"NA"
  levels(d5g) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5g <- ordered(d5g, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5g)
  new.d <- apply_labels(new.d, d5g = "violence to you")
  temp.d <- data.frame (new.d, d5g)  
  
  result<-questionr::freq(temp.d$d5g,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?")
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
n % val%
Never 183 64.9 67.5
Once 9 3.2 3.3
More_than_once 62 22.0 22.9
Dont_know_not_sure 11 3.9 4.1
Prefer_not_to_answer 6 2.1 2.2
NA 11 3.9 NA
Total 282 100.0 100.0 
# h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
  d5h <- as.factor(d[,"d5h"])
# Make "*" to NA
d5h[which(d5h=="*")]<-"NA"
  levels(d5h) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5h <- ordered(d5h, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5h)
  new.d <- apply_labels(new.d, d5h = "swear insult")
  temp.d <- data.frame (new.d, d5h)  
  
  result<-questionr::freq(temp.d$d5h,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?")
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
n % val%
Never 141 50.0 51.8
Once 19 6.7 7.0
More_than_once 80 28.4 29.4
Dont_know_not_sure 23 8.2 8.5
Prefer_not_to_answer 9 3.2 3.3
NA 10 3.5 NA
Total 282 100.0 100.0 
# i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
  d5i <- as.factor(d[,"d5i"])
  # Make "*" to NA
d5i[which(d5i=="*")]<-"NA"
  levels(d5i) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5i <- ordered(d5i, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5i)
  new.d <- apply_labels(new.d, d5i = "touch you sexually")
  temp.d <- data.frame (new.d, d5i)  
  
  result<-questionr::freq(temp.d$d5i,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?")
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
n % val%
Never 246 87.2 90.4
Once 8 2.8 2.9
More_than_once 9 3.2 3.3
Dont_know_not_sure 5 1.8 1.8
Prefer_not_to_answer 4 1.4 1.5
NA 10 3.5 NA
Total 282 100.0 100.0 
# j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
  d5j <- as.factor(d[,"d5j"])
  # Make "*" to NA
d5j[which(d5j=="*")]<-"NA"
  levels(d5j) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5j <- ordered(d5j, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5j)
  new.d <- apply_labels(new.d, d5j = "touch them sexually")
  temp.d <- data.frame (new.d, d5j)  
  
  result<-questionr::freq(temp.d$d5j,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?")
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
n % val%
Never 249 88.3 90.9
Once 10 3.5 3.6
More_than_once 9 3.2 3.3
Dont_know_not_sure 3 1.1 1.1
Prefer_not_to_answer 3 1.1 1.1
NA 8 2.8 NA
Total 282 100.0 100.0 
# k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
  d5k <- as.factor(d[,"d5k"])
  # Make "*" to NA
d5k[which(d5k=="*")]<-"NA"
  levels(d5k) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5k <- ordered(d5k, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5k)
  new.d <- apply_labels(new.d, d5k = "forced to have sex")
  temp.d <- data.frame (new.d, d5k)  
  
  result<-questionr::freq(temp.d$d5k,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "k. How often did anyone at least 5 years older than you or an adult, force you to have sex?")
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
n % val%
Never 262 92.9 95.6
Once 5 1.8 1.8
More_than_once 3 1.1 1.1
Dont_know_not_sure 2 0.7 0.7
Prefer_not_to_answer 2 0.7 0.7
NA 8 2.8 NA
Total 282 100.0 100.0

E1: First indications

  • E1. What were the first indications that suggested that you might have prostate cancer (before you had a prostate biopsy)? Mark all that apply.
    • E1_1: 1=I had a high PSA (‘prostate specific antigen’) test
    • E1_2: 1=My doctor did a digital rectal exam that indicated an abnormality
    • E1_3: 1=I had urinary, sexual, or bowel problems that I went to see my doctor about
    • E1_4: 1=I had bone pain that I went to see my doctor about
    • E1_5: 1=I was fearful I had cancer
    • E1_6: 1=Other
# 1
  e1_1 <- as.factor(d[,"e1_1"])
  levels(e1_1) <- list(High_PSA_test="1")

  new.d <- data.frame(new.d, e1_1)
  new.d <- apply_labels(new.d, e1_1 = "High_PSA_test")
  temp.d <- data.frame (new.d, e1_1)  
  
  result<-questionr::freq(temp.d$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
n % val%
High_PSA_test 209 74.1 100
NA 73 25.9 NA
Total 282 100.0 100 
#2
  e1_2 <- as.factor(d[,"e1_2"])
  levels(e1_2) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_2)
  new.d <- apply_labels(new.d, e1_2 = "digital rectal exam")
  temp.d <- data.frame (new.d, e1_2)  
  
  result<-questionr::freq(temp.d$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
n % val%
Digital_rectal_exam 75 26.6 100
NA 207 73.4 NA
Total 282 100.0 100 
#3
  e1_3 <- as.factor(d[,"e1_3"])
  e1_3[which(e1_3=="*")]<-"NA"
  levels(e1_3) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_3)
  new.d <- apply_labels(new.d, e1_3 = "urinary sexual or bowel problems")
  temp.d <- data.frame (new.d, e1_3)  
  
  result<-questionr::freq(temp.d$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
n % val%
Digital_rectal_exam 50 17.7 100
NA 232 82.3 NA
Total 282 100.0 100 
#4
  e1_4 <- as.factor(d[,"e1_4"])
  e1_4[which(e1_4=="*")]<-"NA"
  levels(e1_4) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_4)
  new.d <- apply_labels(new.d, e1_4 = "bone pain")
  temp.d <- data.frame (new.d, e1_4)  
  
  result<-questionr::freq(temp.d$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
n % val%
Digital_rectal_exam 6 2.1 100
NA 276 97.9 NA
Total 282 100.0 100 
#5
  e1_5 <- as.factor(d[,"e1_5"])
  e1_5[which(e1_5=="*")]<-"NA"
  levels(e1_5) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_5)
  new.d <- apply_labels(new.d, e1_5 = "fearful")
  temp.d <- data.frame (new.d, e1_5)  
  
  result<-questionr::freq(temp.d$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
n % val%
Digital_rectal_exam 15 5.3 100
NA 267 94.7 NA
Total 282 100.0 100

E1 Other: First indications

e1other <- d[,"e1other"]
e1other[which(e1other=="#NAME?")]<-"NA"

  new.d <- data.frame(new.d, e1other)
  new.d <- apply_labels(new.d, e1other = "e1other")
  temp.d <- data.frame (new.d, e1other)
result<-questionr::freq(temp.d$e1other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E1 Other")
E1 Other
n % val%
After my oldest brother died of cancer, my doctor suggested a biopsy 1 0.4 3.7
Age 50 I took a blood test 1 0.4 3.7
Blood in urine 1 0.4 3.7
Check up 1 0.4 3.7
Check up from new doctor. 1 0.4 3.7
Doctor told me 1 0.4 3.7
Don’t know 1 0.4 3.7
Extreme dizzy spell lead to doctor visit/diagnosis 1 0.4 3.7
Family history 1 0.4 3.7
had blood test and PSA test was high 1 0.4 3.7
History of my father, made me self conscious at early age! 1 0.4 3.7
I applied for ins. they did a PSA test, not my Dr. 1 0.4 3.7
I had to urinate every 45 minutes during an outing with a friend. 1 0.4 3.7
I kept having to pee all the time. 1 0.4 3.7
I urinate a lot 1 0.4 3.7
I wanted a PSA test (me) 1 0.4 3.7
Low flow could not go to the bathroom 1 0.4 3.7
My blood PSA was high 1 0.4 3.7
My right testicle was swollen 1 0.4 3.7
No sign ever 1 0.4 3.7
No symptoms 1 0.4 3.7
On 2-28-2016 10:15 AM PSA was 6354 (block) 1 0.4 3.7
Pain in right leg, fearful did not know why. 1 0.4 3.7
Pre Hernia surgery 1 0.4 3.7
Routine rectal exam 1 0.4 3.7
Sexual issues 1 0.4 3.7
Urinary 1 0.4 3.7
NA 255 90.4 NA
Total 282 100.0 100.0

E2: Before diagnosis

  • E2. Before you were diagnosed with prostate cancer:
      1. Did you have any previous prostate biopsies that were negative?
      • 2=Yes
      • 1=No
      • 88=Don’t know
    • If yes, How many?
      • 1=1
      • 2=2
      • 3=3 or more
      1. Did you have any previous PSA blood tests that were considered normal?
      • 2=Yes
      • 1=No
      • 88=Don’t know
    • If yes, How many?
      • 1=1
      • 2=2
      • 3=3
      • 4=4
      • 5=5 or more
# 1
  e2aa <- as.factor(d[,"e2aa"])
# Make "*" to NA
e2aa[which(e2aa=="*")]<-"NA"
  levels(e2aa) <- list(Yes="2",
                      No="1",
                      Dont_know="88")
  e2aa <- ordered(e2aa, c("Yes","No","Dont_know"))
  
  new.d <- data.frame(new.d, e2aa)
  new.d <- apply_labels(new.d, e2aa = "prostate biopsies")
  temp.d <- data.frame (new.d, e2aa)  
  
  result<-questionr::freq(temp.d$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
n % val%
Yes 41 14.5 15.1
No 200 70.9 73.8
Dont_know 30 10.6 11.1
NA 11 3.9 NA
Total 282 100.0 100.0 
#2
  e2ab <- as.factor(d[,"e2ab"])
# Make "*" to NA
e2ab[which(e2ab=="*")]<-"NA"
  levels(e2ab) <- list(One="1",
                      Two="2",
                      Three_more="3")
  e2ab <- ordered(e2ab, c("One","Two","Three_more"))
  
  new.d <- data.frame(new.d, e2ab)
  new.d <- apply_labels(new.d, e2ab = "prostate biopsies_How many")
  temp.d <- data.frame (new.d, e2ab)  
  
  result<-questionr::freq(temp.d$e2ab,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, How many?")
If yes, How many?
n % val%
One 19 6.7 43.2
Two 16 5.7 36.4
Three_more 9 3.2 20.5
NA 238 84.4 NA
Total 282 100.0 100.0 
#3
  e2ba <- as.factor(d[,"e2ba"])
# Make "*" to NA
e2ba[which(e2ba=="*")]<-"NA"
  levels(e2ba) <- list(Yes="2",
                       No="1",
                       Dont_know="88")
  e2ba <- ordered(e2ba, c("Yes","No","Dont_know"))
  
  new.d <- data.frame(new.d, e2ba)
  new.d <- apply_labels(new.d, e2ba = "PSA blood tests")
  temp.d <- data.frame (new.d, e2ba)  
  
  result<-questionr::freq(temp.d$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
n % val%
Yes 114 40.4 44.0
No 84 29.8 32.4
Dont_know 61 21.6 23.6
NA 23 8.2 NA
Total 282 100.0 100.0 
#4
  e2bb <- as.factor(d[,"e2bb"])
  # Make "*" to NA
e2bb[which(e2bb=="*")]<-"NA"
  levels(e2bb) <- list(One="1",
                      Two="2",
                      Three="3",
                      Four="4",
                      Five_more="5")
  e2bb <- ordered(e2bb, c("One","Two","Threem","Four","Five_more"))
  
  new.d <- data.frame(new.d, e2bb)
  new.d <- apply_labels(new.d, e2bb = "PSA blood tests_how many")
  temp.d <- data.frame (new.d, e2bb)  
  
  result<-questionr::freq(temp.d$e2bb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, How many?")
If yes, How many?
n % val%
One 19 6.7 19.8
Two 16 5.7 16.7
Threem 0 0.0 0.0
Four 15 5.3 15.6
Five_more 46 16.3 47.9
NA 186 66.0 NA
Total 282 100.0 100.0

E3: Decision about PSA blood test

  • E3. Which of the following best describes your decision to have the PSA blood test that indicated that you had prostate cancer?
    • 1=I made the decision alone
    • 2=I made the decision together with a family member or friend
    • 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
    • 4= I made the decision together with my doctor, nurse, or health care provider
    • 5=My doctor, nurse, or health care provider made the decision
    • 88=I do not know or remember how the decision was made
  e3 <- as.factor(d[,"e3"])
# Make "*" to NA
e3[which(e3=="*")]<-"NA"
  levels(e3) <- list(Alone="1",
                     With_family_or_friends="2",
                     With_family_and_doctor="3",
                     With_doctor="4",
                     Doctor_made="5",
                     Dont_know_or_remember="88")
  e3 <- ordered(e3, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e3)
  new.d <- apply_labels(new.d, e3 = "decision to have the PSA blood test")
  temp.d <- data.frame (new.d, e3)  
  
  result<-questionr::freq(temp.d$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")
E3
n % val%
Alone 38 13.5 14.1
With_family_or_friends 16 5.7 5.9
With_family_and_doctor 40 14.2 14.8
With_doctor 69 24.5 25.6
Doctor_made 101 35.8 37.4
Dont_know_or_remember 6 2.1 2.2
NA 12 4.3 NA
Total 282 100.0 100.0

E4: Understanding of aggressiveness

  • E4. When you were diagnosed with prostate cancer, what was your understanding of how aggressive your cancer might be (i.e., how likely it was that your cancer might progress).
    • 1=Low risk of progression
    • 2=Intermediate risk of progression
    • 3=High risk of progression
    • 4=Unknown risk of progression
    • 88=Don’t know/Don’t remember
  e4 <- as.factor(d[,"e4"])
# Make "*" to NA
e4[which(e4=="*")]<-"NA"
  levels(e4) <- list(Low_risk="1",
                     Intermediate_risk="2",
                     High_risk="3",
                     Unknown_risk="4",
                     Dont_know_or_remember="88")
  e4 <- ordered(e4, c("Low_risk","Intermediate_risk","High_risk","Unknown_risk","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e4)
  new.d <- apply_labels(new.d, e4 = "how aggressive")
  temp.d <- data.frame (new.d, e4)  
  
  result<-questionr::freq(temp.d$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")
e4
n % val%
Low_risk 90 31.9 33.0
Intermediate_risk 55 19.5 20.1
High_risk 83 29.4 30.4
Unknown_risk 23 8.2 8.4
Dont_know_or_remember 22 7.8 8.1
NA 9 3.2 NA
Total 282 100.0 100.0

E5: Gleason score

  • E5. What was your Gleason score when you were diagnosed with prostate cancer?
    • 1=6 or less
    • 2=7
    • 3=8-10
    • 88=Don’t know
  e5 <- as.factor(d[,"e5"])
# Make "*" to NA
e5[which(e5=="*")]<-"NA"
  levels(e5) <- list(Six_less="1",
                     Seven="2",
                     Eight_to_ten="3",
                     Dont_know="88")
  e5 <- ordered(e5, c("Six_less","Seven","Eight_to_ten","Dont_know"))
  
  new.d <- data.frame(new.d, e5)
  new.d <- apply_labels(new.d, e5 = "Gleason score")
  temp.d <- data.frame (new.d, e5)  
  
  result<-questionr::freq(temp.d$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")
e5
n % val%
Six_less 58 20.6 21.6
Seven 51 18.1 19.0
Eight_to_ten 49 17.4 18.2
Dont_know 111 39.4 41.3
NA 13 4.6 NA
Total 282 100.0 100.0

E6: Understanding of stage

  • E6. What was your understanding of the stage of your prostate cancer when you were diagnosed?
    • 1=Localized, confined to prostate
    • 2=Regional, tumor extended to regions around the prostate
    • 3=Distant, tumor extended to bones or other parts of body
    • 88=Don’t know about the stage
  e6 <- as.factor(d[,"e6"])
# Make "*" to NA
e6[which(e6=="*")]<-"NA"
  levels(e6) <- list(Localized="1",
                     Regional="2",
                     Distant="3",
                     Dont_know="88")
  e6 <- ordered(e6, c("Localized","Regional","Distant","Dont_know"))
  
  new.d <- data.frame(new.d, e6)
  new.d <- apply_labels(new.d, e6 = "Stage")
  temp.d <- data.frame (new.d, e6)  
  
  result<-questionr::freq(temp.d$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")
e6
n % val%
Localized 198 70.2 72.8
Regional 18 6.4 6.6
Distant 8 2.8 2.9
Dont_know 48 17.0 17.6
NA 10 3.5 NA
Total 282 100.0 100.0

E7: MRI guided biopsy

  • E7. Did you have a Magnetic Resonance Imaging (MRI)-guided biopsy to diagnose your cancer? (This is a different type of biopsy than the standard ultrasound biopsy that involves taking 12 random biopsy core samples. Instead, you would be placed in a large donut shaped machine that can be noisy. With assistance from the MRI, 2-3 targeted biopsies would be taken in areas of the tumor shown to be most aggressive.)
    • 2=Yes
    • 1=No
    • 88=Don’t Know
  e7 <- as.factor(d[,"e7"])
# Make "*" to NA
e7[which(e7=="*")]<-"NA"
  levels(e7) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e7 <- ordered(e7, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e7)
  new.d <- apply_labels(new.d, e7 = "Stage")
  temp.d <- data.frame (new.d, e7)  
  
  result<-questionr::freq(temp.d$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")
e7
n % val%
No 131 46.5 49.1
Yes 101 35.8 37.8
Dont_know 35 12.4 13.1
NA 15 5.3 NA
Total 282 100.0 100.0

E8: Decision about treatment

  • E8. How did you make your treatment decision?
    • 1=I made the decision alone
    • 2=I made the decision together with a family member or friend
    • 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
    • 4=I made the decision together with my doctor, nurse, or health care provider
    • 5=My doctor , nurse, or health care provider made the decision
    • 6=I don’t know or remember how the decision was made
  e8 <- as.factor(d[,"e8"])
# Make "*" to NA
e8[which(e8=="*")]<-"NA"
  levels(e8) <- list(Alone="1",
                     With_family_or_friends="2",
                     With_family_and_doctor="3",
                     With_doctor="4",
                     Doctor_made="5",
                     Dont_know_or_remember="88")
  e8 <- ordered(e8, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e8)
  new.d <- apply_labels(new.d, e8 = "treatment decision")
  temp.d <- data.frame (new.d, e8)  
  
  result<-questionr::freq(temp.d$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")
e8
n % val%
Alone 25 8.9 9.4
With_family_or_friends 39 13.8 14.7
With_family_and_doctor 99 35.1 37.4
With_doctor 71 25.2 26.8
Doctor_made 31 11.0 11.7
Dont_know_or_remember 0 0.0 0.0
NA 17 6.0 NA
Total 282 100.0 100.0

E9: The most important factors of tx

  • E9. What were the most important factors you considered in making your treatment decision? Mark all that apply.
    • E9_1: 1=Best chance for cure of my cancer
    • E9_2: 1=Minimize side effects related to sexual function
    • E9_3: 1=Minimize side effects related to urinary function
    • E9_4: 1=Minimize side effects related to bowel function
    • E9_5: 1=Minimize financial cost
    • E9_6: 1=Amount of time and travel required to receive treatments
    • E9_7: 1=Length of recovery time
    • E9_8: 1=Amount of time away from work
    • E9_9: 1=Burden on family members
    • E9_10: 1=Reduce worry and concern about cancer
  e9_1 <- as.factor(d[,"e9_1"])
  levels(e9_1) <- list(Best_for_cure="1")
  new.d <- data.frame(new.d, e9_1)
  new.d <- apply_labels(new.d, e9_1 = "Best for cure")
  temp.d <- data.frame (new.d, e9_1)  
  result<-questionr::freq(temp.d$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
n % val%
Best_for_cure 235 83.3 100
NA 47 16.7 NA
Total 282 100.0 100 
  e9_2 <- as.factor(d[,"e9_2"])
  levels(e9_2) <- list(side_effects_sexual="1")
  new.d <- data.frame(new.d, e9_2)
  new.d <- apply_labels(new.d, e9_2 = "side effects sexual")
  temp.d <- data.frame (new.d, e9_2)  
  result<-questionr::freq(temp.d$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
n % val%
side_effects_sexual 88 31.2 100
NA 194 68.8 NA
Total 282 100.0 100 
  e9_3 <- as.factor(d[,"e9_3"])
  levels(e9_3) <- list(side_effects_urinary="1")
  new.d <- data.frame(new.d, e9_3)
  new.d <- apply_labels(new.d, e9_3 = "side effects urinary")
  temp.d <- data.frame (new.d, e9_3)  
  result<-questionr::freq(temp.d$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
n % val%
side_effects_urinary 76 27 100
NA 206 73 NA
Total 282 100 100 
  e9_4 <- as.factor(d[,"e9_4"])
  levels(e9_4) <- list(side_effects_bowel="1")
  new.d <- data.frame(new.d, e9_4)
  new.d <- apply_labels(new.d, e9_4 = "side effects bowel")
  temp.d <- data.frame (new.d, e9_4)  
  result<-questionr::freq(temp.d$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
n % val%
side_effects_bowel 36 12.8 100
NA 246 87.2 NA
Total 282 100.0 100 
  e9_5 <- as.factor(d[,"e9_5"])
  levels(e9_5) <- list(financial_cost="1")
  new.d <- data.frame(new.d, e9_5)
  new.d <- apply_labels(new.d, e9_5 = "financial cost")
  temp.d <- data.frame (new.d, e9_5)  
  result<-questionr::freq(temp.d$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
n % val%
financial_cost 9 3.2 100
NA 273 96.8 NA
Total 282 100.0 100 
  e9_6 <- as.factor(d[,"e9_6"])
  levels(e9_6) <- list(time_and_travel="1")
  new.d <- data.frame(new.d, e9_6)
  new.d <- apply_labels(new.d, e9_6 = "time and travel")
  temp.d <- data.frame (new.d, e9_6)  
  result<-questionr::freq(temp.d$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
n % val%
time_and_travel 20 7.1 100
NA 262 92.9 NA
Total 282 100.0 100 
  e9_7 <- as.factor(d[,"e9_7"])
  levels(e9_7) <- list(recovery_time="1")
  new.d <- data.frame(new.d, e9_7)
  new.d <- apply_labels(new.d, e9_7 = "recovery time")
  temp.d <- data.frame (new.d, e9_7)  
  result<-questionr::freq(temp.d$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
n % val%
recovery_time 47 16.7 100
NA 235 83.3 NA
Total 282 100.0 100 
  e9_8 <- as.factor(d[,"e9_8"])
  levels(e9_8) <- list(time_away_from_work="1")
  new.d <- data.frame(new.d, e9_8)
  new.d <- apply_labels(new.d, e9_8 = "time away from work")
  temp.d <- data.frame (new.d, e9_8)  
  result<-questionr::freq(temp.d$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
n % val%
time_away_from_work 22 7.8 100
NA 260 92.2 NA
Total 282 100.0 100 
  e9_9 <- as.factor(d[,"e9_9"])
  levels(e9_9) <- list(family_burden="1")
  new.d <- data.frame(new.d, e9_9)
  new.d <- apply_labels(new.d, e9_9 = "family burden")
  temp.d <- data.frame (new.d, e9_9)  
  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
n % val%
family_burden 32 11.3 100
NA 250 88.7 NA
Total 282 100.0 100 
  e9_10 <- as.factor(d[,"e9_10"])
  levels(e9_10) <- list(Reduce_worry_concern="1")
  new.d <- data.frame(new.d, e9_10)
  new.d <- apply_labels(new.d, e9_10 = "Reduce worry and concern")
  temp.d <- data.frame (new.d, e9_10)  
  result<-questionr::freq(temp.d$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
n % val%
Reduce_worry_concern 129 45.7 100
NA 153 54.3 NA
Total 282 100.0 100

E10: Recieved treatment

  • E10. Please mark all the treatments that you have received for your prostate cancer? Mark all that apply.
    • E10_1: 1=Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
    • E10_2: 1=Active Surveillance or watchful waiting
    • E10_3: 1=Prostate surgery (prostatectomy)
    • E10_4: 1=Radiation to the prostate
    • E10_5: 1=Hormonal treatments
    • E10_6: 1=Provenge/immunotherapy (Sipuleucel T)
    • E10_7: 1=Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
    • E10_8: 1=Other treatments to the prostate (HIFU (High Intensity Focused Ultrasound), RFA (Radio Frequency Ablation), laser, focal therapy, cryotherapy (freezing of the prostate))
  e10_1 <- as.factor(d[,"e10_1"])
  levels(e10_1) <- list(no_treatment="1")
  new.d <- data.frame(new.d, e10_1)
  new.d <- apply_labels(new.d, e10_1 = "no treatment")
  temp.d <- data.frame (new.d, e10_1)  
  result<-questionr::freq(temp.d$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
n % val%
no_treatment 20 7.1 100
NA 262 92.9 NA
Total 282 100.0 100 
  e10_2 <- as.factor(d[,"e10_2"])
  levels(e10_2) <- list(Active_Surveillance="1")
  new.d <- data.frame(new.d, e10_2)
  new.d <- apply_labels(new.d, e10_2 = "Active Surveillance")
  temp.d <- data.frame (new.d, e10_2)  
  result<-questionr::freq(temp.d$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
n % val%
Active_Surveillance 56 19.9 100
NA 226 80.1 NA
Total 282 100.0 100 
  e10_3 <- as.factor(d[,"e10_3"])
  levels(e10_3) <- list(prostatectomy="1")
  new.d <- data.frame(new.d, e10_3)
  new.d <- apply_labels(new.d, e10_3 = "prostatectomy")
  temp.d <- data.frame (new.d, e10_3)  
  result<-questionr::freq(temp.d$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
n % val%
prostatectomy 110 39 100
NA 172 61 NA
Total 282 100 100 
  e10_4 <- as.factor(d[,"e10_4"])
  levels(e10_4) <- list(Radiation="1")
  new.d <- data.frame(new.d, e10_4)
  new.d <- apply_labels(new.d, e10_4 = "Radiation")
  temp.d <- data.frame (new.d, e10_4)  
  result<-questionr::freq(temp.d$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
n % val%
Radiation 91 32.3 100
NA 191 67.7 NA
Total 282 100.0 100 
  e10_5 <- as.factor(d[,"e10_5"])
  levels(e10_5) <- list(Hormonal_treatments="1")
  new.d <- data.frame(new.d, e10_5)
  new.d <- apply_labels(new.d, e10_5 = "Hormonal treatments")
  temp.d <- data.frame (new.d, e10_5)  
  result<-questionr::freq(temp.d$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
n % val%
Hormonal_treatments 36 12.8 100
NA 246 87.2 NA
Total 282 100.0 100 
  e10_6 <- as.factor(d[,"e10_6"])
  levels(e10_6) <- list(Provenge_immunotherapy="1")
  new.d <- data.frame(new.d, e10_6)
  new.d <- apply_labels(new.d, e10_6 = "Provenge immunotherapy")
  temp.d <- data.frame (new.d, e10_6)  
  result<-questionr::freq(temp.d$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
n % val%
Provenge_immunotherapy 5 1.8 100
NA 277 98.2 NA
Total 282 100.0 100 
  e10_7 <- as.factor(d[,"e10_7"])
  levels(e10_7) <- list(Chemotherapy="1")
  new.d <- data.frame(new.d, e10_7)
  new.d <- apply_labels(new.d, e10_7 = "Chemotherapy")
  temp.d <- data.frame (new.d, e10_7)  
  result<-questionr::freq(temp.d$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
n % val%
Chemotherapy 10 3.5 100
NA 272 96.5 NA
Total 282 100.0 100 
  e10_8 <- as.factor(d[,"e10_8"])
  levels(e10_8) <- list(Other="1")
  new.d <- data.frame(new.d, e10_8)
  new.d <- apply_labels(new.d, e10_8 = "Other")
  temp.d <- data.frame (new.d, e10_8)  
  result<-questionr::freq(temp.d$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
n % val%
Other 9 3.2 100
NA 273 96.8 NA
Total 282 100.0 100

E10-3 Prostatectomy

  • E10_3. Prostate surgery (prostatectomy), indicate which type(s):
    • E10_3_1: 1=Robotic or laproscopic surgery resulting in removal of the prostate
    • E10_3_2: 1=Open surgical removal of the prostate (using a long incision)
    • E10_3_3: 1=Had surgery but unsure of type
  e10_3_1 <- as.factor(d[,"e10_3_1"])
  levels(e10_3_1) <- list(Robotic_laproscopic_surgery="1")
  new.d <- data.frame(new.d, e10_3_1)
  new.d <- apply_labels(new.d, e10_3_1 = "Robotic or laproscopic surgery")
  temp.d <- data.frame (new.d, e10_3_1)  
  result<-questionr::freq(temp.d$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
n % val%
Robotic_laproscopic_surgery 132 46.8 100
NA 150 53.2 NA
Total 282 100.0 100 
  e10_3_2 <- as.factor(d[,"e10_3_2"])
  levels(e10_3_2) <- list(Open_surgical_removal="1")
  new.d <- data.frame(new.d, e10_3_2)
  new.d <- apply_labels(new.d, e10_3_2 = "Open surgical removal")
  temp.d <- data.frame (new.d, e10_3_2)  
  result<-questionr::freq(temp.d$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
n % val%
Open_surgical_removal 10 3.5 100
NA 272 96.5 NA
Total 282 100.0 100 
  e10_3_3 <- as.factor(d[,"e10_3_3"])
  levels(e10_3_3) <- list(unsure_of_type="1")
  new.d <- data.frame(new.d, e10_3_3)
  new.d <- apply_labels(new.d, e10_3_3 = "unsure of type")
  temp.d <- data.frame (new.d, e10_3_3)  
  result<-questionr::freq(temp.d$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
n % val%
unsure_of_type 11 3.9 100
NA 271 96.1 NA
Total 282 100.0 100

E10-4 Radiation

  • E10_4. Radiation to the prostate, indicate which type(s):
    • E10_4_1: 1=External beam radiation, where beams are aimed from the outside of your body (including IMRT (Intensity Modulated Radiation Therapy), IGRT (Image-Guided Radiation Therapy), arc therapy, proton beam, cyberknife, or 3D-conformal beam therapy)
    • E10_4_2: 1 = Insertion of radiation seed/roods (brachytherapy)
    • E10_4_3: 1=Other types of radiation therapy, or unsure of what type
  e10_4_1 <- as.factor(d[,"e10_4_1"])
  levels(e10_4_1) <- list(External_beam_radiation="1")
  new.d <- data.frame(new.d, e10_4_1)
  new.d <- apply_labels(new.d, e10_4_1 = "External beam radiation")
  temp.d <- data.frame (new.d, e10_4_1)  
  result<-questionr::freq(temp.d$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
n % val%
External_beam_radiation 85 30.1 100
NA 197 69.9 NA
Total 282 100.0 100 
  e10_4_2 <- as.factor(d[,"e10_4_2"])
  levels(e10_4_2) <- list(brachytherapy="1")
  new.d <- data.frame(new.d, e10_4_2)
  new.d <- apply_labels(new.d, e10_4_2 = "brachytherapy")
  temp.d <- data.frame (new.d, e10_4_2)  
  result<-questionr::freq(temp.d$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
n % val%
brachytherapy 29 10.3 100
NA 253 89.7 NA
Total 282 100.0 100 
  e10_4_3 <- as.factor(d[,"e10_4_3"])
  levels(e10_4_3) <- list(Other_types="1")
  new.d <- data.frame(new.d, e10_4_3)
  new.d <- apply_labels(new.d, e10_4_3 = "Other types")
  temp.d <- data.frame (new.d, e10_4_3)  
  result<-questionr::freq(temp.d$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
n % val%
Other_types 13 4.6 100
NA 269 95.4 NA
Total 282 100.0 100

E10-5 Hormonal treatments

  • E10_5. Hormonal treatments, indicate which type(s):
    • E10_5_1: 1=Hormone shots (Lupron, Zoladex, Firmagon, Eligard, Vantas)
    • E10_5_2: 1= Surgical removal of testicles (orchiectomy)
    • E10_5_3: 1=Casodex (bicalutamide) or Eulexin (flutamide) pills
    • E10_5_4: 1=Zytiga (abiraterone) or Xtandi (enzalutamide) pills
    • E10_5_5: 1=Had hormone treatment, but unsure of type
  e10_5_1 <- as.factor(d[,"e10_5_1"])
  levels(e10_5_1) <- list(Hormone_shots="1")
  new.d <- data.frame(new.d, e10_5_1)
  new.d <- apply_labels(new.d, e10_5_1 = "Hormone shots")
  temp.d <- data.frame (new.d, e10_5_1)  
  result<-questionr::freq(temp.d$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
n % val%
Hormone_shots 49 17.4 100
NA 233 82.6 NA
Total 282 100.0 100 
  e10_5_2 <- as.factor(d[,"e10_5_2"])
  levels(e10_5_2) <- list(orchiectomy="1")
  new.d <- data.frame(new.d, e10_5_2)
  new.d <- apply_labels(new.d, e10_5_2 = "orchiectomy")
  temp.d <- data.frame (new.d, e10_5_2)  
  result<-questionr::freq(temp.d$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
n % val%
orchiectomy 3 1.1 100
NA 279 98.9 NA
Total 282 100.0 100 
  e10_5_3 <- as.factor(d[,"e10_5_3"])
  levels(e10_5_3) <- list(Casodex_Eulexin="1")
  new.d <- data.frame(new.d, e10_5_3)
  new.d <- apply_labels(new.d, e10_5_3 = "Casodex or Eulexin pills")
  temp.d <- data.frame (new.d, e10_5_3)  
  result<-questionr::freq(temp.d$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
n % val%
Casodex_Eulexin 11 3.9 100
NA 271 96.1 NA
Total 282 100.0 100 
  e10_5_4 <- as.factor(d[,"e10_5_4"])
  levels(e10_5_4) <- list(Zytiga_Xtandi="1")
  new.d <- data.frame(new.d, e10_5_4)
  new.d <- apply_labels(new.d, e10_5_4 = "Zytiga or Xtandi pills")
  temp.d <- data.frame (new.d, e10_5_4)  
  result<-questionr::freq(temp.d$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
n % val%
Zytiga_Xtandi 6 2.1 100
NA 276 97.9 NA
Total 282 100.0 100 
  e10_5_5 <- as.factor(d[,"e10_5_5"])
  levels(e10_5_5) <- list(unsure_type="1")
  new.d <- data.frame(new.d, e10_5_5)
  new.d <- apply_labels(new.d, e10_5_5 = "unsure of type")
  temp.d <- data.frame (new.d, e10_5_5)  
  result<-questionr::freq(temp.d$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
n % val%
unsure_type 14 5 100
NA 268 95 NA
Total 282 100 100

E11: Treatment decision

  • E11. Your treatment decision: How true is each of the following statements for you?
      1. I had all the information I needed when a treatment was chosen for my prostate cancer
      1. My doctors told me the whole story about the effects of treatment
      1. I knew the right questions to ask my doctor
      1. I had enough time to make a decision about my treatment
      1. I am satisfied with the choices I made in treating my prostate cancer
      1. I would recommend the treatment I had to a close relative or friend
      • 1=Not at all
      • 2=A little bit
      • 3=Somewhat
      • 4=Quite a bit
      • 5=Very much
  e11a <- as.factor(d[,"e11a"])
# Make "*" to NA
e11a[which(e11a=="*")]<-"NA"
  levels(e11a) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11a)
  new.d <- apply_labels(new.d, e11a = "all info")
  temp.d <- data.frame (new.d, e11a)  
  result<-questionr::freq(temp.d$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
n % val%
Not_at_all 10 3.5 3.8
A_little_bit 6 2.1 2.3
Somewhat 34 12.1 12.8
Quite_a_bit 63 22.3 23.8
Very_much 152 53.9 57.4
NA 17 6.0 NA
Total 282 100.0 100.0 
  e11b <- as.factor(d[,"e11b"])
# Make "*" to NA
e11b[which(e11b=="*")]<-"NA"
  levels(e11b) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11b)
  new.d <- apply_labels(new.d, e11b = "be told about effects")
  temp.d <- data.frame (new.d, e11b)  
  result<-questionr::freq(temp.d$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
n % val%
Not_at_all 6 2.1 2.2
A_little_bit 9 3.2 3.4
Somewhat 40 14.2 14.9
Quite_a_bit 66 23.4 24.6
Very_much 147 52.1 54.9
NA 14 5.0 NA
Total 282 100.0 100.0 
  e11c <- as.factor(d[,"e11c"])
  # Make "*" to NA
e11c[which(e11c=="*")]<-"NA"
  levels(e11c) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11c)
  new.d <- apply_labels(new.d, e11c = "right questions to ask")
  temp.d <- data.frame (new.d, e11c)  
  result<-questionr::freq(temp.d$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
n % val%
Not_at_all 31 11.0 11.6
A_little_bit 33 11.7 12.4
Somewhat 86 30.5 32.2
Quite_a_bit 48 17.0 18.0
Very_much 69 24.5 25.8
NA 15 5.3 NA
Total 282 100.0 100.0 
  e11d <- as.factor(d[,"e11d"])
  # Make "*" to NA
e11d[which(e11d=="*")]<-"NA"
  levels(e11d) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11d)
  new.d <- apply_labels(new.d, e11d = "enough time to decide")
  temp.d <- data.frame (new.d, e11d)  
  result<-questionr::freq(temp.d$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
n % val%
Not_at_all 9 3.2 3.4
A_little_bit 20 7.1 7.5
Somewhat 42 14.9 15.7
Quite_a_bit 76 27.0 28.5
Very_much 120 42.6 44.9
NA 15 5.3 NA
Total 282 100.0 100.0 
  e11e <- as.factor(d[,"e11e"])
  # Make "*" to NA
e11e[which(e11e=="*")]<-"NA"
  levels(e11e) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11e)
  new.d <- apply_labels(new.d, e11e = "satisfied with the choices")
  temp.d <- data.frame (new.d, e11e)  
  result<-questionr::freq(temp.d$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
n % val%
Not_at_all 12 4.3 4.5
A_little_bit 8 2.8 3.0
Somewhat 42 14.9 15.7
Quite_a_bit 43 15.2 16.1
Very_much 162 57.4 60.7
NA 15 5.3 NA
Total 282 100.0 100.0 
  e11f <- as.factor(d[,"e11f"])
  # Make "*" to NA
e11f[which(e11f=="*")]<-"NA"
  levels(e11f) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11f)
  new.d <- apply_labels(new.d, e11f = "would recommend")
  temp.d <- data.frame (new.d, e11f)  
  result<-questionr::freq(temp.d$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
n % val%
Not_at_all 21 7.4 8.0
A_little_bit 12 4.3 4.6
Somewhat 37 13.1 14.2
Quite_a_bit 45 16.0 17.2
Very_much 146 51.8 55.9
NA 21 7.4 NA
Total 282 100.0 100.0

E12: Instructions from doctors or nurses

  • E12. Have you ever received instructions from a doctor, nurse, or other health professional about who you should see for routine prostate cancer checkups or monitoring?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e12 <- as.factor(d[,"e12"])
# Make "*" to NA
e12[which(e12=="*")]<-"NA"
  levels(e12) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e12 <- ordered(e12, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e12)
  new.d <- apply_labels(new.d, e12 = "received instructions")
  temp.d <- data.frame (new.d, e12)  
  
  result<-questionr::freq(temp.d$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")
e12
n % val%
No 37 13.1 13.7
Yes 223 79.1 82.3
Dont_know 11 3.9 4.1
NA 11 3.9 NA
Total 282 100.0 100.0

E13: # of PSA blood test

  • E13. Since your prostate cancer diagnosis, how many times have you had a PSA blood test?
    • 0=None
    • 1=1
    • 2=2
    • 3=3
    • 4=4 or more
    • 88=Don’t know/not sure
  e13 <- as.factor(d[,"e13"])
# Make "*" to NA
e13[which(e13=="*")]<-"NA"
  levels(e13) <- list(None="0",
                      One="1",
                      Two="2",
                     Three="3",
                     Four_more="4",
                     Dont_know="88")
  e13 <- ordered(e13, c("None","One","Two","Three","Four_more","Dont_know"))
  
  new.d <- data.frame(new.d, e13)
  new.d <- apply_labels(new.d, e13 = "times of PSA blood test")
  temp.d <- data.frame (new.d, e13)  
  
  result<-questionr::freq(temp.d$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")
e13
n % val%
None 5 1.8 1.8
One 4 1.4 1.5
Two 5 1.8 1.8
Three 26 9.2 9.6
Four_more 208 73.8 76.5
Dont_know 24 8.5 8.8
NA 10 3.5 NA
Total 282 100.0 100.0

E14: Be told PSA was rising

  • E14. Since diagnosis or treatment, have you ever been told that your PSA was rising?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e14 <- as.factor(d[,"e14"])
# Make "*" to NA
e14[which(e14=="*")]<-"NA"
  levels(e14) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e14 <- ordered(e14, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e14)
  new.d <- apply_labels(new.d, e14 = "been told PSA was rising")
  temp.d <- data.frame (new.d, e14)  
  
  result<-questionr::freq(temp.d$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")
e14
n % val%
No 185 65.6 68.8
Yes 68 24.1 25.3
Dont_know 16 5.7 5.9
NA 13 4.6 NA
Total 282 100.0 100.0

E15: Recurred or got worse

  • E15. Since you were diagnosed, did your doctor ever tell you that your prostate cancer came back (recurred) or progressed (got worse)?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e15 <- as.factor(d[,"e15"])
# Make "*" to NA
e15[which(e15=="*")]<-"NA"
  levels(e15) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e15 <- ordered(e15, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e15)
  new.d <- apply_labels(new.d, e15 = "been told recurred progressed")
  temp.d <- data.frame (new.d, e15)  
  
  result<-questionr::freq(temp.d$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")
e15
n % val%
No 230 81.6 84.9
Yes 32 11.3 11.8
Dont_know 9 3.2 3.3
NA 11 3.9 NA
Total 282 100.0 100.0

F1: Height

  • F1. How tall are you?
  f1cm <- d[,"f1cm"]
 
  new.d <- data.frame(new.d, f1cm)
  new.d <- apply_labels(new.d, f1cm = "height in cm")
  temp.d <- data.frame (new.d, f1cm)  
  
  result<-questionr::freq(temp.d$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How tall are you? (cm)")
How tall are you? (cm)
n % val%
11 1 0.4 33.3
178 1 0.4 33.3
2 1 0.4 33.3
NA 279 98.9 NA
Total 282 100.0 100.0

F2: Weight

  • F2. How much do you current weight?
  f2lbs <- d[,"f2lbs"]
  new.d <- data.frame(new.d, f2lbs)
  new.d <- apply_labels(new.d, f2lbs = "weight in lbs")
  temp.d <- data.frame (new.d, f2lbs)  
  result<-questionr::freq(temp.d$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (lbs)")
How much do you current weight? (lbs)
n % val%
*5 1 0.4 0.4
1 1 0.4 0.4
112 1 0.4 0.4
114 1 0.4 0.4
138 1 0.4 0.4
140 1 0.4 0.4
145 2 0.7 0.8
146 1 0.4 0.4
150 4 1.4 1.6
152 1 0.4 0.4
153 1 0.4 0.4
154 1 0.4 0.4
155 2 0.7 0.8
156 1 0.4 0.4
157 2 0.7 0.8
158 1 0.4 0.4
159 1 0.4 0.4
160 4 1.4 1.6
162 3 1.1 1.2
163 1 0.4 0.4
165 5 1.8 2.0
167 5 1.8 2.0
168 1 0.4 0.4
170 5 1.8 2.0
172 2 0.7 0.8
173 1 0.4 0.4
174 1 0.4 0.4
175 6 2.1 2.4
176 1 0.4 0.4
177 4 1.4 1.6
178 4 1.4 1.6
179 1 0.4 0.4
180 9 3.2 3.6
182 4 1.4 1.6
184 4 1.4 1.6
185 8 2.8 3.2
186 1 0.4 0.4
187 2 0.7 0.8
188 1 0.4 0.4
189 6 2.1 2.4
190 8 2.8 3.2
192 2 0.7 0.8
193 1 0.4 0.4
195 7 2.5 2.8
196 1 0.4 0.4
197 1 0.4 0.4
198 1 0.4 0.4
199 3 1.1 1.2
2 1 0.4 0.4
2 * 1 0.4 0.4
2 2 1 0.4 0.4
200 9 3.2 3.6
202 2 0.7 0.8
205 6 2.1 2.4
206 1 0.4 0.4
207 1 0.4 0.4
208 3 1.1 1.2
209 3 1.1 1.2
210 8 2.8 3.2
212 1 0.4 0.4
214 2 0.7 0.8
215 9 3.2 3.6
219 2 0.7 0.8
220 9 3.2 3.6
222 1 0.4 0.4
223 1 0.4 0.4
225 1 0.4 0.4
226 2 0.7 0.8
228 1 0.4 0.4
230 1 0.4 0.4
232 2 0.7 0.8
233 1 0.4 0.4
234 1 0.4 0.4
235 3 1.1 1.2
238 1 0.4 0.4
240 5 1.8 2.0
242 2 0.7 0.8
243 1 0.4 0.4
244 1 0.4 0.4
245 4 1.4 1.6
249 1 0.4 0.4
250 3 1.1 1.2
251 1 0.4 0.4
252 1 0.4 0.4
254 1 0.4 0.4
260 2 0.7 0.8
261 1 0.4 0.4
262 1 0.4 0.4
263 1 0.4 0.4
265 6 2.1 2.4
270 1 0.4 0.4
275 3 1.1 1.2
285 1 0.4 0.4
287 1 0.4 0.4
289 1 0.4 0.4
290 2 0.7 0.8
294 1 0.4 0.4
305 1 0.4 0.4
308 1 0.4 0.4
355 1 0.4 0.4
440 1 0.4 0.4
65 1 0.4 0.4
81 1 0.4 0.4
NA 35 12.4 NA
Total 282 100.0 100.0 
  f2kgs <- d[,"f2kgs"]
  new.d <- data.frame(new.d, f2kgs)
  new.d <- apply_labels(new.d, f2kgs = "weight in lbs")
  temp.d <- data.frame (new.d, f2kgs)  
  result<-questionr::freq(temp.d$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
n % val%
0 2 0.7 40
2 1 0.4 20
70 1 0.4 20
75 1 0.4 20
NA 277 98.2 NA
Total 282 100.0 100

F3: Exercise frequency

  • F3. How many days per week do you typically get moderate or strenuous exercise (such as heavy lifting, shop work, construction or farm work, home repair, gardening, bowling, golf, jogging, basketball, riding a bike, etc.)?
    • 4=5-7 times per week
    • 3=3-4 times per week
    • 2=1-2 times per week
    • 1=Less than once per week/do not exercise
  f3 <- as.factor(d[,"f3"])
# Make "*" to NA
f3[which(f3=="*")]<-"NA"
  levels(f3) <- list(Per_week_5_7="4",
                     Per_week_3_4="3",
                     Per_week_1_2="2",
                     Per_week_less_1="1")
  f3 <- ordered(f3, c("Per_week_5_7","Per_week_3_4","Per_week_1_2","Per_week_less_1"))
  
  new.d <- data.frame(new.d, f3)
  new.d <- apply_labels(new.d, f3 = "exercise")
  temp.d <- data.frame (new.d, f3)  
  
  result<-questionr::freq(temp.d$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
n % val% %cum val%cum
Per_week_5_7 53 18.8 20.9 18.8 20.9
Per_week_3_4 79 28.0 31.2 46.8 52.2
Per_week_1_2 70 24.8 27.7 71.6 79.8
Per_week_less_1 51 18.1 20.2 89.7 100.0
NA 29 10.3 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

F4: Minutes of exercise

  • F4. On those days that you do moderate or strenuous exercise, how many minutes did you typically exercise at this level?
    • 2=Less than 30 minutes
    • 3=30 minutes – 1 hour
    • 4=More than 1 hour
    • 1=Do not exercise
  f4 <- as.factor(d[,"f4"])
# Make "*" to NA
f4[which(f4=="*")]<-"NA"
  levels(f4) <- list(Less_than_30_min="2",
                     Between_30_min_1_hour="3",
                     More_than_1_hour="4",
                     Do_not_exercise="1")
  f4 <- ordered(f4, c("Less_than_30_min","Between_30_min_1_hour","More_than_1_hour","Do_not_exercise"))
  
  new.d <- data.frame(new.d, f4)
  new.d <- apply_labels(new.d, f4 = "how many minutes exercise")
  temp.d <- data.frame (new.d, f4)  
  
  result<-questionr::freq(temp.d$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F4")
F4
n % val% %cum val%cum
Less_than_30_min 47 16.7 18.7 16.7 18.7
Between_30_min_1_hour 101 35.8 40.1 52.5 58.7
More_than_1_hour 69 24.5 27.4 77.0 86.1
Do_not_exercise 35 12.4 13.9 89.4 100.0
NA 30 10.6 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

F5: Drink alcohol frequency

  • F5. In the past month, about how often do you have at least one drink of any alcoholic beverage such as beer, wine, a malt beverage, or liquor? One drink is equivalent to a 12 oz beer, a 5 oz glass of wine, or a drink with one shot of liquor.
    • 6=Everyday
    • 5=5-6 times per week
    • 4=3-4 times per week
    • 3=1-2 times per week
    • 2=Fewer than once per week
    • 1=Did not drink
  f5 <- as.factor(d[,"f5"])
# Make "*" to NA
f5[which(f5=="*")]<-"NA"
  levels(f5) <- list(Everyday="6",
                     Per_week_5_6_times="5",
                     Per_week_3_4_times="4",
                     Per_week_1_2_times="3",
                     Per_week_fewer_once="2",
                     Not_drink="1")
  f5 <- ordered(f5, c("Everyday","Per_week_5_6_times","Per_week_3_4_times","Per_week_1_2_times","Per_week_fewer_once","Not_drink"))
  
  new.d <- data.frame(new.d, f5)
  new.d <- apply_labels(new.d, f5 = "how often drink")
  temp.d <- data.frame (new.d, f5)  
  
  result<-questionr::freq(temp.d$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")
f5
n % val% %cum val%cum
Everyday 17 6.0 6.2 6.0 6.2
Per_week_5_6_times 12 4.3 4.4 10.3 10.6
Per_week_3_4_times 40 14.2 14.7 24.5 25.3
Per_week_1_2_times 29 10.3 10.6 34.8 35.9
Per_week_fewer_once 52 18.4 19.0 53.2 54.9
Not_drink 123 43.6 45.1 96.8 100.0
NA 9 3.2 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

F6: How many drinks

  • F6. When you drank during the past month, how many drinks do you have on a typical occasion?
    • 3=3 or more drinks
    • 2=1-2 drinks
    • 1=Did not drink
  f6 <- as.factor(d[,"f6"])
# Make "*" to NA
f6[which(f6=="*")]<-"NA"
  levels(f6) <- list(Three_or_more="3",
                     One_to_two_drinks="2",
                     Not_drink="1")
  f6 <- ordered(f6, c("Three_or_more","One_to_two_drinks","Not_drink"))
  
  new.d <- data.frame(new.d, f6)
  new.d <- apply_labels(new.d, f6 = "how many drinks")
  temp.d <- data.frame (new.d, f6)  
  
  result<-questionr::freq(temp.d$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")
f6
n % val% %cum val%cum
Three_or_more 32 11.3 11.9 11.3 11.9
One_to_two_drinks 116 41.1 43.0 52.5 54.8
Not_drink 122 43.3 45.2 95.7 100.0
NA 12 4.3 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

F7: Smoking history

  • F7. Have you ever smoked at least 100 cigarettes in your lifetime?
    • 1=No
    • 2=Yes
  • F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
    • 555 = “Less than 10”
    • 777 = “75+”
  • F7a. How many cigarettes do you (or did you) usually smoke per day?
    • 1=1-5
    • 2=6-10
    • 3=11-20
    • 4=21-30
    • 5=31+
  • F7b. Have you quit smoking?
    • 1=No
    • 2=Yes
  • F7BAge. If yes, At what age did you quit?
    • 555 = “Less than 10”
    • 777 = “75+”
  f7 <- as.factor(d[,"f7"])
# Make "*" to NA
f7[which(f7=="*")]<-"NA"
  levels(f7) <- list(Yes="2",
                     No="1")
  f7 <- ordered(f7, c("No","Yes"))
  
  new.d <- data.frame(new.d, f7)
  new.d <- apply_labels(new.d, f7 = "smoke")
  temp.d <- data.frame (new.d, f7)  
  
  result<-questionr::freq(temp.d$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
n % val% %cum val%cum
No 157 55.7 58.6 55.7 58.6
Yes 111 39.4 41.4 95.0 100.0
NA 14 5.0 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  f7age <- d[,"f7age"]
  f7age[which(f7age=="555")]<-"Less_than_10"
  f7age[which(f7age=="777")]<-"More_than_75"

  new.d <- data.frame(new.d, f7age)
  new.d <- apply_labels(new.d, f7age = "age start to smoke")
  temp.d <- data.frame (new.d, f7age)  
  
  result<-questionr::freq(temp.d$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
n % val%
11 1 0.4 1.2
12 1 0.4 1.2
14 4 1.4 4.7
15 6 2.1 7.1
16 7 2.5 8.2
17 6 2.1 7.1
18 15 5.3 17.6
19 12 4.3 14.1
20 7 2.5 8.2
21 4 1.4 4.7
22 4 1.4 4.7
23 2 0.7 2.4
25 6 2.1 7.1
28 3 1.1 3.5
30 2 0.7 2.4
32 1 0.4 1.2
34 1 0.4 1.2
35 2 0.7 2.4
40 1 0.4 1.2
NA 197 69.9 NA
Total 282 100.0 100.0 
  f7a <- as.factor(d[,"f7a"])
  # Make "*" to NA
f7a[which(f7a=="*")]<-"NA"
  levels(f7a) <- list(One_to_five="1",
                     Six_to_ten="2",
                     Eleven_to_twenty="3",
                     Twentyone_to_Thirty="4",
                     Older_31="5")
  f7a <- ordered(f7a, c("One_to_five","Six_to_ten","Eleven_to_twenty","Twentyone_to_Thirty","Older_31"))

  new.d <- data.frame(new.d, f7a)
  new.d <- apply_labels(new.d, f7a = "How many cigarettes per day")
  temp.d <- data.frame (new.d, f7a)  
  
  result<-questionr::freq(temp.d$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
n % val% %cum val%cum
One_to_five 42 14.9 37.8 14.9 37.8
Six_to_ten 40 14.2 36.0 29.1 73.9
Eleven_to_twenty 21 7.4 18.9 36.5 92.8
Twentyone_to_Thirty 6 2.1 5.4 38.7 98.2
Older_31 2 0.7 1.8 39.4 100.0
NA 171 60.6 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0 
  f7b <- as.factor(d[,"f7b"])
    # Make "*" to NA
f7b[which(f7b=="*")]<-"NA"
  levels(f7b) <- list(No="1",
                     Yes="2")

  new.d <- data.frame(new.d, f7b)
  new.d <- apply_labels(new.d, f7b = "quit smoking")
  temp.d <- data.frame (new.d, f7b)  
  
  result<-questionr::freq(temp.d$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
n % val%
No 27 9.6 23.1
Yes 90 31.9 76.9
NA 165 58.5 NA
Total 282 100.0 100.0 
  f7bage <- d[,"f7bage"]
  f7bage[which(f7bage=="555")]<-"Less_than_10"
  f7bage[which(f7bage=="777")]<-"More_than_75"

  new.d <- data.frame(new.d, f7bage)
  new.d <- apply_labels(new.d, f7bage = "age quit smoking")
  temp.d <- data.frame (new.d, f7bage)  
  
  result<-questionr::freq(temp.d$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
n % val%
12 1 0.4 1.2
16 2 0.7 2.3
17 1 0.4 1.2
20 2 0.7 2.3
21 1 0.4 1.2
22 1 0.4 1.2
23 3 1.1 3.5
24 1 0.4 1.2
25 2 0.7 2.3
27 2 0.7 2.3
28 2 0.7 2.3
29 1 0.4 1.2
30 3 1.1 3.5
31 2 0.7 2.3
32 1 0.4 1.2
34 2 0.7 2.3
35 4 1.4 4.7
38 1 0.4 1.2
39 1 0.4 1.2
40 3 1.1 3.5
41 2 0.7 2.3
42 2 0.7 2.3
44 2 0.7 2.3
45 4 1.4 4.7
47 2 0.7 2.3
48 3 1.1 3.5
49 1 0.4 1.2
50 7 2.5 8.1
51 1 0.4 1.2
52 1 0.4 1.2
53 2 0.7 2.3
54 3 1.1 3.5
55 3 1.1 3.5
57 2 0.7 2.3
58 2 0.7 2.3
59 1 0.4 1.2
60 1 0.4 1.2
61 2 0.7 2.3
64 4 1.4 4.7
65 3 1.1 3.5
67 1 0.4 1.2
72 1 0.4 1.2
NA 196 69.5 NA
Total 282 100.0 100.0

G1: Marital status

  • G1. What is your current marital status?
    • 1=Married, or living with a partner
    • 2=Separated
    • 3=Divorced
    • 4=Widowed
    • 5=Never Married
  g1 <- as.factor(d[,"g1"])
  # Make "*" to NA
g1[which(g1=="*")]<-"NA"
  levels(g1) <- list(Married_partner="1",
                     Separated="2",
                     Divorced="3",
                     Widowed="4",
                     Never_Married="5")
  g1 <- ordered(g1, c("Married_partner","Separated","Divorced","Widowed","Never_Married"))
  
  new.d <- data.frame(new.d, g1)
  new.d <- apply_labels(new.d, g1 = "marital status")
  temp.d <- data.frame (new.d, g1)  
  
  result<-questionr::freq(temp.d$g1,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")
g1
n % val% %cum val%cum
Married_partner 193 68.4 70.2 68.4 70.2
Separated 6 2.1 2.2 70.6 72.4
Divorced 42 14.9 15.3 85.5 87.6
Widowed 10 3.5 3.6 89.0 91.3
Never_Married 24 8.5 8.7 97.5 100.0
NA 7 2.5 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

G2: With whom do you live

  • G2. With whom do you live? Mark all that apply.
    • G2_1: 1=Live alone
    • G2_2: 1=A spouse or partner
    • G2_3: 1=Other family
    • G2_4: 1=Other people (non-family)
    • G2_5: 1=Pets
  g2_1 <- as.factor(d[,"g2_1"])
  levels(g2_1) <- list(Live_alone="1")

  new.d <- data.frame(new.d, g2_1)
  new.d <- apply_labels(new.d, g2_1 = "Live alone")
  temp.d <- data.frame (new.d, g2_1)  
  
  result<-questionr::freq(temp.d$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
n % val%
Live_alone 41 14.5 100
NA 241 85.5 NA
Total 282 100.0 100 
  g2_2 <- as.factor(d[,"g2_2"])
  levels(g2_2) <- list(spouse_partner="1")

  new.d <- data.frame(new.d, g2_2)
  new.d <- apply_labels(new.d, g2_2 = "A spouse or partner")
  temp.d <- data.frame (new.d, g2_2)  
  
  result<-questionr::freq(temp.d$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
n % val%
spouse_partner 199 70.6 100
NA 83 29.4 NA
Total 282 100.0 100 
  g2_3 <- as.factor(d[,"g2_3"])
  levels(g2_3) <- list(Other_family="1")

  new.d <- data.frame(new.d, g2_3)
  new.d <- apply_labels(new.d, g2_3 = "Other family")
  temp.d <- data.frame (new.d, g2_3)  
  
  result<-questionr::freq(temp.d$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
n % val%
Other_family 35 12.4 100
NA 247 87.6 NA
Total 282 100.0 100 
  g2_4 <- as.factor(d[,"g2_4"])
  levels(g2_4) <- list(Other_non_family="1")

  new.d <- data.frame(new.d, g2_4)
  new.d <- apply_labels(new.d, g2_4 = "Other people (non-family)")
  temp.d <- data.frame (new.d, g2_4)  
  
  result<-questionr::freq(temp.d$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
n % val%
Other_non_family 13 4.6 100
NA 269 95.4 NA
Total 282 100.0 100 
  g2_5 <- as.factor(d[,"g2_5"])
  levels(g2_5) <- list(Pets="1")

  new.d <- data.frame(new.d, g2_5)
  new.d <- apply_labels(new.d, g2_5 = "Pets")
  temp.d <- data.frame (new.d, g2_5)  
  
  result<-questionr::freq(temp.d$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
n % val%
Pets 21 7.4 100
NA 261 92.6 NA
Total 282 100.0 100

G3: Identify yourself

  • G3. How do you identify yourself?
    • 1=Straight/heterosexual
    • 2=Bisexual
    • 3=Gay/homosexual/same gender loving
    • 4=Other
    • 99=Prefer not to answer
  g3 <- as.factor(d[,"g3"])
  # Make "*" to NA
g3[which(g3=="*")]<-"NA"
  levels(g3) <- list(heterosexual="1",
                      Bisexual="2",
                       homosexual="3",
                       Other="4",
                       Prefer_not_to_answer="99")
  g3 <- ordered(g3, c("heterosexual","Bisexual","homosexual","Other","Prefer_not_to_answer"))

  new.d <- data.frame(new.d, g3)
  new.d <- apply_labels(new.d, g3 = "identify yourself")
  temp.d <- data.frame (new.d, g3)  
  
  result<-questionr::freq(temp.d$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")
g3
n % val%
heterosexual 258 91.5 95.2
Bisexual 4 1.4 1.5
homosexual 6 2.1 2.2
Other 0 0.0 0.0
Prefer_not_to_answer 3 1.1 1.1
NA 11 3.9 NA
Total 282 100.0 100.0

G3 Other: Identify yourself

g3other <- d[,"g3other"]
  new.d <- data.frame(new.d, g3other)
  new.d <- apply_labels(new.d, g3other = "g3other")
  temp.d <- data.frame (new.d, g3other)
result<-questionr::freq(temp.d$g3other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G3 Other")
G3 Other
n % val%
Man 1 0.4 100
NA 281 99.6 NA
Total 282 100.0 100

G4: Education

  • G4. What is the HIGHEST level of education you, your father, and your mother have completed?
    • 1=Grade school or less
    • 2=Some high school
    • 3=High school graduate or GED
    • 4=Vocational school
    • 5=Some college
    • 6=Associate’s degree
    • 7=College graduate (Bachelor’s degree)
    • 8=Some graduate education
    • 9=Graduate degree
    • 88=Don’t know
  g4a <- as.factor(d[,"g4a"])
  # Make "*" to NA
g4a[which(g4a=="*")]<-"NA"
  levels(g4a) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9")
  g4a <- ordered(g4a, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree"))

  new.d <- data.frame(new.d, g4a)
  new.d <- apply_labels(new.d, g4a = "education")
  temp.d <- data.frame (new.d, g4a)  
  
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
n % val%
Grade_school_or_less 1 0.4 0.4
Some_high_school 7 2.5 2.8
High_school_graduate_GED 43 15.2 17.2
Vocational_school 2 0.7 0.8
Some_college 72 25.5 28.8
Associate_degree 30 10.6 12.0
College_graduate 40 14.2 16.0
Some_graduate_education 7 2.5 2.8
Graduate_degree 48 17.0 19.2
NA 32 11.3 NA
Total 282 100.0 100.0 
  g4b <- as.factor(d[,"g4b"])
    # Make "*" to NA
g4b[which(g4b=="*")]<-"NA"
  levels(g4b) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Dont_know="88")
  g4b <- ordered(g4b, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know"))

  new.d <- data.frame(new.d, g4b)
  new.d <- apply_labels(new.d, g4b = "education-father")
  temp.d <- data.frame (new.d, g4b)  
  
  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
n % val%
Grade_school_or_less 48 17.0 19.4
Some_high_school 25 8.9 10.1
High_school_graduate_GED 59 20.9 23.9
Vocational_school 6 2.1 2.4
Some_college 12 4.3 4.9
Associate_degree 15 5.3 6.1
College_graduate 10 3.5 4.0
Some_graduate_education 3 1.1 1.2
Graduate_degree 13 4.6 5.3
Dont_know 56 19.9 22.7
NA 35 12.4 NA
Total 282 100.0 100.0 
  g4c <- as.factor(d[,"g4c"])
    # Make "*" to NA
g4c[which(g4c=="*")]<-"NA"
  levels(g4c) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Dont_know="88")
  g4c <- ordered(g4c, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know"))

  new.d <- data.frame(new.d, g4c)
  new.d <- apply_labels(new.d, g4c = "education-mother")
  temp.d <- data.frame (new.d, g4c)  
  
  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
n % val%
Grade_school_or_less 39 13.8 15.5
Some_high_school 36 12.8 14.3
High_school_graduate_GED 77 27.3 30.7
Vocational_school 10 3.5 4.0
Some_college 10 3.5 4.0
Associate_degree 15 5.3 6.0
College_graduate 16 5.7 6.4
Some_graduate_education 5 1.8 2.0
Graduate_degree 11 3.9 4.4
Dont_know 32 11.3 12.7
NA 31 11.0 NA
Total 282 100.0 100.0

G5: Job

  • G5. Which one of the following best describes what you currently do?
    • 1=Currently working full-time
    • 2=Currently working part-time
    • 3=Looking for work, unemployed
    • 4=Retired
    • 5=On disability permanently
    • 6=On disability for a period of time (on sick leave or paternity leave or disability leave for other reasons)
    • 7=Volunteer work/work without pay
    • 8=Other
  g5 <- as.factor(d[,"g5"])
  # Make "*" to NA
g5[which(g5=="*")]<-"NA"
  levels(g5) <- list(full_time="1",
                     part_time="2",
                     unemployed="3",
                     Retired="4",
                     disability_permanently="5",
                     disability_for_a_time="6",
                     Volunteer_work="7",
                     Other="8")
  g5 <- ordered(g5, c("full_time","part_time","unemployed","Retired","disability_permanently","disability_for_a_time", "Volunteer_work","Other"))

  new.d <- data.frame(new.d, g5)
  new.d <- apply_labels(new.d, g5 = "job")
  temp.d <- data.frame (new.d, g5)  
  
  result<-questionr::freq(temp.d$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")
g5
n % val%
full_time 80 28.4 30.0
part_time 17 6.0 6.4
unemployed 7 2.5 2.6
Retired 129 45.7 48.3
disability_permanently 24 8.5 9.0
disability_for_a_time 4 1.4 1.5
Volunteer_work 2 0.7 0.7
Other 4 1.4 1.5
NA 15 5.3 NA
Total 282 100.0 100.0

G5 Other: job

g5other <- d[,"g5other"]
  new.d <- data.frame(new.d, g5other)
  new.d <- apply_labels(new.d, g5other = "g5other")
  temp.d <- data.frame (new.d, g5other)
result<-questionr::freq(temp.d$g5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G5 Other")
G5 Other
n % val%
My family help with my bills and rent 1 0.4 11.1
Retired and worked as consultant 1 0.4 11.1
Retired but assisting partner at her business daily 1 0.4 11.1
Retired fighting for permanent from VA. 1 0.4 11.1
Self employed 2 0.7 22.2
Self employed. 1 0.4 11.1
Social Security 1 0.4 11.1
Vet disability Vietnam 1 0.4 11.1
NA 273 96.8 NA
Total 282 100.0 100.0

G6: Health insurance

  • G6. What kind of health insurance or health care coverage do you currently have? Mark all that apply.
    • G6_1: 1=Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
    • G6_2: 1=Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
    • G6_3: 1=Insurance purchased directly from an insurance company (by you or another family member)
    • G6_4: 1=Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
    • G6_5: 1= Medicaid or other state provided insurance
    • G6_6: 1=Medicare/government insurance
    • G6_7: 1=VA/Military Facility (including those who have ever used or enrolled for VA health care)
    • G6_8: 1=I do not have any medical insurance
  g6_1 <- as.factor(d[,"g6_1"])
  levels(g6_1) <- list(Insurance_employer="1")
  new.d <- data.frame(new.d, g6_1)
  new.d <- apply_labels(new.d, g6_1 = "Insurance_employer")
  temp.d <- data.frame (new.d, g6_1)  
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_1. Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
G6_1. Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_employer 103 36.5 100
NA 179 63.5 NA
Total 282 100.0 100 
  g6_2 <- as.factor(d[,"g6_2"])
  levels(g6_2) <- list(Insurance_family="1")
  new.d <- data.frame(new.d, g6_2)
  new.d <- apply_labels(new.d, g6_2 = "Insurance_family")
  temp.d <- data.frame (new.d, g6_2)  
  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_2. Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
G6_2. Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_family 49 17.4 100
NA 233 82.6 NA
Total 282 100.0 100 
  g6_3 <- as.factor(d[,"g6_3"])
  levels(g6_3) <- list(Insurance_insurance_company="1")
  new.d <- data.frame(new.d, g6_3)
  new.d <- apply_labels(new.d, g6_3 = "Insurance_insurance_company")
  temp.d <- data.frame (new.d, g6_3)  
  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_3. Insurance purchased directly from an insurance company (by you or another family member)")
G6_3. Insurance purchased directly from an insurance company (by you or another family member)
n % val%
Insurance_insurance_company 14 5 100
NA 268 95 NA
Total 282 100 100 
  g6_4 <- as.factor(d[,"g6_4"])
  levels(g6_4) <- list(Insurance_exchange="1")
  new.d <- data.frame(new.d, g6_4)
  new.d <- apply_labels(new.d, g6_4 = "Insurance_exchange")
  temp.d <- data.frame (new.d, g6_4)  
  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_4. Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
G6_4. Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
n % val%
Insurance_exchange 9 3.2 100
NA 273 96.8 NA
Total 282 100.0 100 
  g6_5 <- as.factor(d[,"g6_5"])
  levels(g6_5) <- list(Medicaid_state="1")
  new.d <- data.frame(new.d, g6_5)
  new.d <- apply_labels(new.d, g6_5 = "Medicaid_state")
  temp.d <- data.frame (new.d, g6_5)  
  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_5. Medicaid or other state provided insurance")
G6_5. Medicaid or other state provided insurance
n % val%
Medicaid_state 31 11 100
NA 251 89 NA
Total 282 100 100 
  g6_6 <- as.factor(d[,"g6_6"])
  levels(g6_6) <- list(Medicare_government="1")
  new.d <- data.frame(new.d, g6_6)
  new.d <- apply_labels(new.d, g6_6 = "Medicare_government")
  temp.d <- data.frame (new.d, g6_6)  
  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_6. Medicare/government insurance")
G6_6. Medicare/government insurance
n % val%
Medicare_government 100 35.5 100
NA 182 64.5 NA
Total 282 100.0 100 
  g6_7 <- as.factor(d[,"g6_7"])
  levels(g6_7) <- list(VA_Military="1")
  new.d <- data.frame(new.d, g6_7)
  new.d <- apply_labels(new.d, g6_7 = "VA_Military")
  temp.d <- data.frame (new.d, g6_7)  
  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_7. VA/Military Facility (including those who have ever used or enrolled for VA health care)")
G6_7. VA/Military Facility (including those who have ever used or enrolled for VA health care)
n % val%
VA_Military 35 12.4 100
NA 247 87.6 NA
Total 282 100.0 100 
  g6_8 <- as.factor(d[,"g6_8"])
  levels(g6_8) <- list(Do_not_have="1")
  new.d <- data.frame(new.d, g6_8)
  new.d <- apply_labels(new.d, g6_8 = "Do_not_have")
  temp.d <- data.frame (new.d, g6_8)  
  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_8. I do not have any medical insurance")
G6_8. I do not have any medical insurance
n % val%
Do_not_have 3 1.1 100
NA 279 98.9 NA
Total 282 100.0 100

G7: Income

  • G7. What is your best estimate of your TOTAL FAMILY INCOME from all sources, before taxes, in the last calendar year? “Total family income” refers to your income PLUS the income of all family members living in this household (including cohabiting partners, and armed forces members living at home). This includes money from pay checks, government benefit programs, child support, social security, retirement funds, unemployment benefits, and disability.
    • 1=Less than $15,000
    • 2=$15,000 to $35,999
    • 3=$36,000 to $45,999
    • 4=$46,000 to $65,999
    • 5=$66,000 to $99,999
    • 6=$100,000 to $149,999
    • 7=$150,000 to $199,999
    • 8= $200,000 or more
  g7 <- as.factor(d[,"g7"])
  # Make "*" to NA
g7[which(g7=="*")]<-"NA"
  levels(g7) <- list(Less_than_15000="1",
                     Between_15000_35999="2",
                     Between_36000_45999="3",
                     Between_46000_65999="4",
                     Between_66000_99999="5",
                     Between_100000_149999= "6",
                     Between_150000_199999="7",
                     More_than_200000="8")
  g7 <- ordered(g7, c("Less_than_15000","Between_15000_35999","Between_36000_45999","Between_46000_65999","Between_66000_99999","Between_100000_149999", "Between_150000_199999","More_than_200000"))

  new.d <- data.frame(new.d, g7)
  new.d <- apply_labels(new.d, g7 = "income")
  temp.d <- data.frame (new.d, g7)  
  
  result<-questionr::freq(temp.d$g7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g7")
g7
n % val% %cum val%cum
Less_than_15000 30 10.6 11.6 10.6 11.6
Between_15000_35999 29 10.3 11.2 20.9 22.9
Between_36000_45999 28 9.9 10.9 30.9 33.7
Between_46000_65999 33 11.7 12.8 42.6 46.5
Between_66000_99999 50 17.7 19.4 60.3 65.9
Between_100000_149999 48 17.0 18.6 77.3 84.5
Between_150000_199999 20 7.1 7.8 84.4 92.2
More_than_200000 20 7.1 7.8 91.5 100.0
NA 24 8.5 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

G8: # people supported by income

  • G8. In the last calendar year, how many people, including yourself, were supported by your family income?
    • 1=1
    • 2=2
    • 3=3
    • 4=4
    • 5=5 or more
  g8 <- as.factor(d[,"g8"])
  # Make "*" to NA
g8[which(g8=="*")]<-"NA"
  levels(g8) <- list(One="1",
                     Two="2",
                     Three="3",
                     Four="4",
                     Five_or_more="5")
  g8 <- ordered(g8, c("One","Two","Three","Four","Five_or_more"))

  new.d <- data.frame(new.d, g8)
  new.d <- apply_labels(new.d, g8 = "people supported by income")
  temp.d <- data.frame (new.d, g8)  
  
  result<-questionr::freq(temp.d$g8,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g8")
g8
n % val% %cum val%cum
One 68 24.1 26.1 24.1 26.1
Two 116 41.1 44.4 65.2 70.5
Three 43 15.2 16.5 80.5 87.0
Four 18 6.4 6.9 86.9 93.9
Five_or_more 16 5.7 6.1 92.6 100.0
NA 21 7.4 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

G9: Worry about finance

  • G9. How worried were you or your family about being able to pay your normal monthly bills, including rent, mortgage, and/or other costs:
      1. During young adult life (up to age 30):
      1. Age 31 (up to just before prostate cancer diagnosis):
      1. Current (from prostate cancer diagnosis to present):
      • 1=Not at all worried
      • 2=A little worried
      • 3=Somewhat worried
      • 4=Very worried
  g9a <- as.factor(d[,"g9a"])
  # Make "*" to NA
g9a[which(g9a=="*")]<-"NA"
  levels(g9a) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9a)
  new.d <- apply_labels(new.d, g9a = "young adult life")
  temp.d <- data.frame (new.d, g9a)  
  result<-questionr::freq(temp.d$g9a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. During young adult life (up to age 30)")
a. During young adult life (up to age 30)
n % val%
Not_worried 110 39.0 41.0
A_little_worried 74 26.2 27.6
Somewhat_worried 53 18.8 19.8
Very_worried 31 11.0 11.6
NA 14 5.0 NA
Total 282 100.0 100.0 
  g9b <- as.factor(d[,"g9b"])
    # Make "*" to NA
g9b[which(g9b=="*")]<-"NA"
  levels(g9b) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9b)
  new.d <- apply_labels(new.d, g9b = "age 31 up to before dx")
  temp.d <- data.frame (new.d, g9b)  
  result<-questionr::freq(temp.d$g9b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Age 31 (up to just before prostate cancer diagnosis)")
b. Age 31 (up to just before prostate cancer diagnosis)
n % val%
Not_worried 123 43.6 47.1
A_little_worried 74 26.2 28.4
Somewhat_worried 43 15.2 16.5
Very_worried 21 7.4 8.0
NA 21 7.4 NA
Total 282 100.0 100.0 
  g9c <- as.factor(d[,"g9c"])
    # Make "*" to NA
g9c[which(g9c=="*")]<-"NA"
  levels(g9c) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9c)
  new.d <- apply_labels(new.d, g9c = "current")
  temp.d <- data.frame (new.d, g9c)  
  result<-questionr::freq(temp.d$g9c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Current (from prostate cancer diagnosis to present)")
c. Current (from prostate cancer diagnosis to present)
n % val%
Not_worried 153 54.3 57.5
A_little_worried 56 19.9 21.1
Somewhat_worried 26 9.2 9.8
Very_worried 31 11.0 11.7
NA 16 5.7 NA
Total 282 100.0 100.0

G10:Own or rent a house

  • G10. Is the home you live in:
    • 1=Owned or being bought by you (or someone in the household)?
    • 2=Rented for money?
    • 3=Other
  g10 <- as.factor(d[,"g10"])
  # Make "*" to NA
g10[which(g10=="*")]<-"NA"
  levels(g10) <- list(Owned="1",
                     Rented="2",
                     Other="3")
  g10 <- ordered(g10, c("Owned","Rented","Other"))

  new.d <- data.frame(new.d, g10)
  new.d <- apply_labels(new.d, g10 = "Own or rent a house")
  temp.d <- data.frame (new.d, g10)  
  
  result<-questionr::freq(temp.d$g10,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g10")
g10
n % val% %cum val%cum
Owned 180 63.8 67.7 63.8 67.7
Rented 80 28.4 30.1 92.2 97.7
Other 6 2.1 2.3 94.3 100.0
NA 16 5.7 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

G10 Other: Own or rent a house

g10other <- d[,"g10other"]
  new.d <- data.frame(new.d, g10other)
  new.d <- apply_labels(new.d, g10other = "g10other")
  temp.d <- data.frame (new.d, g10other)
result<-questionr::freq(temp.d$g10other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G10 Other")
G10 Other
n % val%
Apartment manager 1 0.4 8.3
Family home 1 0.4 8.3
Free rent for now - friend. 1 0.4 8.3
Homeless 1 0.4 8.3
I live with a friend 1 0.4 8.3
I’m renting an apartment 1 0.4 8.3
Live in apartment complex 1 0.4 8.3
Owned by my employer 1 0.4 8.3
Owned by my wife 1 0.4 8.3
Rent 1 0.4 8.3
Rent. 1 0.4 8.3
RV 1 0.4 8.3
NA 270 95.7 NA
Total 282 100.0 100.0

G11:Lose current sources

  • G11. If you lost all your current source(s) of household income (your paycheck, public assistance, or other forms of income), how long could you continue to live at your current address and standard of living?
    • 1=Less than 1 month
    • 2=1 to 2 months
    • 3=3 to 6 months
    • 4=More than 6 months
  g11 <- as.factor(d[,"g11"])
  # Make "*" to NA
g11[which(g11=="*")]<-"NA"
  levels(g11) <- list(Less_than_1_month="1",
                     One_to_two_month="2",
                     Three_to_six_month="3",
                     More_than_6_months="4")
  g11 <- ordered(g11, c("Less_than_1_month","One_to_two_month","Three_to_six_month","More_than_6_months"))

  new.d <- data.frame(new.d, g11)
  new.d <- apply_labels(new.d, g11 = "ose current sources")
  temp.d <- data.frame (new.d, g11)  
  
  result<-questionr::freq(temp.d$g11,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g11")
g11
n % val% %cum val%cum
Less_than_1_month 37 13.1 14.0 13.1 14.0
One_to_two_month 59 20.9 22.3 34.0 36.4
Three_to_six_month 64 22.7 24.2 56.7 60.6
More_than_6_months 104 36.9 39.4 93.6 100.0
NA 18 6.4 NA 100.0 NA
Total 282 100.0 100.0 100.0 100.0

G12: Today’s date

  • G12. Please enter today’s date.
  g12 <- as.Date(d[ , "g12"], format="%m/%d/%y")
  new.d <- data.frame(new.d, g12)
  new.d <- apply_labels(new.d, g12 = "today’s date")
  #temp.d <- data.frame (new.d.1, g12) 
  
  summarytools::view(dfSummary(new.d$g12, style = 'grid',
                               max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 g12 [labelled, Date] today’s date
min : 2019-05-18
med : 2020-06-16
max : 2020-12-15
range : 1y 6m 27d
 183 distinct values 7 (2.5%)

Generated by summarytools 1.0.0 (R version 3.6.3)
2021-12-09